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Articles Home » Data Science » Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part6 - Deep Learning

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part6 - Deep Learning

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

· Posted by Superadmin on November 13 2019 · 0 Comments · 533 Reads · Print
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