Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part6 - Deep Learning
Posted by Superadmin on November 13 2019 00:18:15

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


01.1. What to Expect from this Part


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


01.2. What is Machine Learning.html
https://drive.google.com/open?id=


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.1. Introduction to Neural Networks


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.1.1 Course Notes - Section 2.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.2. Introduction to Neural Networks.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.3. Training the Model


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.3.1 Course Notes - Section 2.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.4. Training the Model.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.5. Types of Machine Learning


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.6. Types of Machine Learning.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.7. The Linear Model (Linear Algebraic Version)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.8. The Linear Model.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.9. The Linear Model with Multiple Inputs


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.10. The Linear Model with Multiple Inputs.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.11. The Linear model with Multiple Inputs and Multiple Outputs


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.12. The Linear model with Multiple Inputs and Multiple Outputs.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.13. Graphical Representation of Simple Neural Networks


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.14. Graphical Representation of Simple Neural Networks.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.15. What is the Objective Function


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.16. What is the Objective Function.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.17. Common Objective Functions L2-norm Loss


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.18. Common Objective Functions L2-norm Loss.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.19. Common Objective Functions Cross-Entropy Loss


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.20. Common Objective Functions Cross-Entropy Loss.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.21. Optimization Algorithm 1-Parameter Gradient Descent


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.21.1 GD-function-example.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.22. Optimization Algorithm 1-Parameter Gradient Descent.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.23. Optimization Algorithm n-Parameter Gradient Descent


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


02.24. Optimization Algorithm n-Parameter Gradient Descent.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.1. Basic NN Example (Part 1)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.1.1 Shortcuts-for-Jupyter.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.1.2 Bais NN Example Part 1.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.2. Basic NN Example (Part 2)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.2.1 Basic NN Example (Part 2).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.3. Basic NN Example (Part 3)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.3.1 Basic NN Example (Part 3).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.4. Basic NN Example (Part 4)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.4.1 Basic NN Example (Part 4).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5. Basic NN Example Exercises.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.1 Basic NN Example Exercise 5 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.2 Basic NN Example (All Exercises).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.3 Basic NN Example Exercise 4 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.4 Basic NN Example Exercise 1 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.5 Basic NN Example Exercise 2 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.6 Basic NN Example Exercise 3d Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.7 Basic NN Example Exercise 3b Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.8 Basic NN Example Exercise 3c Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.9 Basic NN Example Exercise 3a Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


03.5.10 Basic NN Example Exercise 6 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.1. How to Install TensorFlow


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.1.1 Shortcuts-for-Jupyter.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.2. A Note on Installation of Packages in Anaconda.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.3. TensorFlow Outline and Logic


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.4. Actual Introduction to TensorFlow


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.4.1 Shortcuts-for-Jupyter.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.5. Types of File Formats, supporting Tensors


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.5.1 Basic NN Example with TensorFlow (Part 1).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.6.1 Basic NN Example with TensorFlow (Part 2).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.7. Basic NN Example with TF Loss Function and Gradient Descent


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.7.1 Basic NN Example with TensorFlow (Part 3).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.8. Basic NN Example with TF Model Output


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.8.1 Basic NN Example with TensorFlow (Complete).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9. Basic NN Example with TF Exercises.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.1 Basic NN Example with TensorFlow Exercise 2.4 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.3 Basic NN Example with TensorFlow (All Exercises).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.4 Basic NN Example with TensorFlow Exercise 3 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.5 Basic NN Example with TensorFlow Exercise 2.2 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.6 Basic NN Example with TensorFlow Exercise 4 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.7 Basic NN Example with TensorFlow Exercise 1 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


04.9.8 Basic NN Example with TensorFlow Exercise 2.3 Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.1. What is a Layer


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.1.1 Course Notes - Section 6.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.2. What is a Deep Net


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.2.1 Course Notes - Section 6.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.3. Digging into a Deep Net


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.4. Non-Linearities and their Purpose


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.5. Activation Functions


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.6. Activation Functions Softmax Activation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.7. Backpropagation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.8. Backpropagation picture


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.9. Backpropagation - A Peek into the Mathematics of Optimization.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.9. Backpropagation - A Peek into the Mathematics of Optimization.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


05.9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.1. What is Overfitting


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.2. Underfitting and Overfitting for Classification


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.3. What is Validation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.4. Training, Validation, and Test Datasets


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.5. N-Fold Cross Validation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


06.6. Early Stopping or When to Stop Training


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


07.1. What is Initialization


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


07.2. Types of Simple Initializations


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


07.3. State-of-the-Art Method - (Xavier) Glorot Initialization


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.1. Stochastic Gradient Descent


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.2. Problems with Gradient Descent


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.3. Momentum


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.5. Learning Rate Schedules Visualized


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop )


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


08.7. Adam (Adaptive Moment Estimation)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


09.1. Preprocessing Introduction


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


09.2. Types of Basic Preprocessing


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


09.3. Standardization


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


09.4. Preprocessing Categorical Data


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


09.5. Binary and One-Hot Encoding


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.1. MNIST What is the MNIST Dataset


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.2. MNIST How to Tackle the MNIST


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.3. MNIST Relevant Packages


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.3.1 TensorFlow MNIST Part 1 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.4. MNIST Model Outline


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.4.1 TensorFlow MNIST Part 2 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.5. MNIST Loss and Optimization Algorithm


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.5.1 TensorFlow MNIST Part 3 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.6. Calculating the Accuracy of the Model


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.6.1 TensorFlow MNIST Part 4 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.7. MNIST Batching and Early Stopping


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.7.1 TensorFlow MNIST Part 5 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.8. MNIST Learning


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.8.1 TensorFlow MNIST Part 6 with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.9. MNIST Results and Testing


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.9.1 TensorFlow MNIST Complete Code with Comments.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.10. MNIST Exercises.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.10.1 TensorFlow MNIST All Exercises.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11. MNIST Solutions.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.1 TensorFlow MNIST 'Time' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.2 TensorFlow MNIST '1. Width' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.3 TensorFlow MNIST '3. Width and Depth' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.4 TensorFlow MNIST '2. Depth' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.5 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.6 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.9 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


10.11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.1. Business Case Getting acquainted with the datase


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.1.1 Audiobooks_data.csv


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.2. Business Case Outlining the Solution


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.3. The Importance of Working with a Balanced Dataset


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.4. MNIST Model Outline


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.4. Business Case Preprocessing


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.4.1 Audiobooks Preprocessing.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.5. Business Case Preprocessing Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.5.1 Preprocessing Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.6. Creating a Data Provider


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.6.1 Creating a Data Provider (Class).html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.7. Business Case Model Outline


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.7.1 TensorFlow Business Case Model Outline.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.8. Business Case Optimization


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.8.1 TensorFlow Business Case Optimization.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.9. Business Case Interpretation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.9.1 TensorFlow Business Case Interpretation.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.10. Business Case Testing the Model


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.11. Business Case A Comment on the Homework


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.11.1 TensorFlow Business Case Homework.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.12. Business Case Final Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


11.12.1 TensorFlow Business Case Homework.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.1. Summary of What You Learned


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.2. What's Further out there in terms of Machine Learning


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.3. An overview of CNNs


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.4. DeepMind and Deep Learning.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.5. An Overview of RNNs


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part 6 Deep Learning

with Iliya


12.6. An Overview of non-NN Approaches


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Introduction to Deep Learning 02. Deep Learning - Introduction to Neural Networks 03. Deep Learning - How to Build a Neural Network from Scratch with NumPy 04. Deep Learning - TensorFlow Introduction 05. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks 06. Deep Learning - Overfitting 07. Deep Learning - Initialization 08. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules 09. Deep Learning - Preprocessing 10. Deep Learning - Classifying on the MNIST Dataset 11. Deep Learning - Business Case Example 12. Deep Learning - Conclusion