Become an AI and Machine Learning Specialist, Part II
Posted by Superadmin on January 22 2019 02:22:23

Become an AI and Machine Learning Specialist, Part II

 

 

 

Branch out and extensively develop your knowledge of machine learning capabilities and techniques across tech platforms. In this learning path, you can begin to master natural language processing and deep learning, and take your AI and machine learning skills to the next level.
Expand your basic knowledge of algorithms.
Develop a more advanced understanding of how to build algorithms.
Extend your mastery of deep learning across diffrerent platforms and toolsets.

 

 

 

 

 

 

01

Building Deep Learning Applications with Keras 2.0 with Adam Geitgey

1h 24m • COURSE
Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. In this course, learn how to install Keras and use it to build a simple deep learning model. Explore the many powerful pre-trained deep learning models included in Keras and how to use them. Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras. When you wrap up this course, you'll be ready to start building and deploying your own models with Keras.
Topics include:
  • What's Keras?
  • Using Keras vs. TensorFlow
  • Training a deep learning model
  • Using a pre-trained deep learning model
  • Monitoring a Keras model with TensorBoard
  • Using a trained Keras model in Google Cloud
02

Building and Deploying Deep Learning Applications with TensorFlow with Adam Geitgey

1h 46m • COURSE
TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.
Topics include:
  • What's TensorFlow?
  • Hardware, software, and language requirements
  • Creating a TensorFlow model
  • Training a deep learning model with TensorFlow
  • Visualizing the computational graph
  • Adding custom visualizations to TensorBoard
  • Exporting models for use with Google Cloud
03

Machine Learning & AI: Advanced Decision Trees with Keith McCormick

1h 16m • COURSE
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Topics include:
  • Understanding QUEST functions and applications
  • C5.0 concepts and practical applications
  • Understanding information gain
  • Random forests
  • Boosting and bagging
  • Costs and priors

 
04

Building a Recommendation System with Python Machine Learning & AI with Lillian Pierson, P.E.

1h 38m • COURSE
Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
Topics include:
  • Working with recommendation systems
  • Evaluating similarity based on correlation
  • Building a popularity-based recommender
  • Classification-based recommendations
  • Making a collaborative filtering system
  • Content-based recommender systems
  • Evaluating recommenders
05

Amazon Web Services Machine Learning Essential Training with Lynn Langit

3h 7m • COURSE
Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. In this course, learn about patterns, services, processes, and best practices for designing and implementing machine learning using AWS. Instructor Lynn Langit takes a look at general machine learning concepts, including key machine learning algorithm types. She also examines available service types, such as AWS Machine Learning, Lex, Polly, and Rekognition, which you can use to predict image and video labels. Plus, she steps through how to work with platforms like AWS SageMaker, which includes hosted Jupyter notebooks.
Topics include:
  • How machine learning is used in analytics
  • AWS AI servers vs. platforms
  • Predicting using Polly text-to-speech
  • Predicting using Rekognition for video
  • Using Lex to build a conversational application
  • Using the AWS Machine Learning service to train, host, and predict
  • Working with MXNet in Databricks
  • Working with EMR for machine learning
06

Neural Networks and Convolutional Neural Networks Essential Training with Jonathan Fernandes

1h 19m • COURSE
Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.

Topics include:
Neurons and artificial neurons
Components of neural networks
Neural network visualization
Neural network implementation in Keras
Compiling and training a neural network model
Accuracy and evaluation of a neural network model
Convolutional neural networks in Keras
Enhancements to convolutional neural networks
Working with VGG16
07

Machine Learning and AI Foundations: Clustering and Association with Keith McCormick

3h 22m • COURSE
Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.

Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.

All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
Topics include:
  • What is unsupervised learning?
  • Cluster and distance-based measures
  • Hierarchical cluster analysis
  • K-means cluster analysis
  • Visualizing and reporting cluster solutions
  • Cluster methods for categorical variables
  • Anomaly detection
  • Association rules
  • Sequence detection
08

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity

Building Deep Learning Applications with Keras 2.0 with Adam Geitgey

1h 24m • COURSE
Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. In this course, learn how to install Keras and use it to build a simple deep learning model. Explore the many powerful pre-trained deep learning models included in Keras and how to use them. Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras. When you wrap up this course, you'll be ready to start building and deploying your own models with Keras.
Topics include:
  • What's Keras?
  • Using Keras vs. TensorFlow
  • Training a deep learning model
  • Using a pre-trained deep learning model
  • Monitoring a Keras model with TensorBoard
  • Using a trained Keras model in Google Cloud

0. INTRODUCTION
001 Welcome
002 What you should know
003 Using the exercise files



1. Keras Overview
004 What is Keras_
005 TensorFlow and Theano backends
006 Using Keras vs. TensorFlow



2. Setting Up Keras
007 Installing Keras with the TensorFlow backend on macOS
008 Installing Keras with the TensorFlow backend on Windows



3. Creating a Neural Network in Keras
009 The train-test-evaluation flow
010 Keras Sequential API
011 Pre-processing training data
012 Define a Keras model using the Sequential API



4. Training Models
013 Training and evaluating the model
014 Making predictions
015 Saving and loading models



5. Pre-Trained Models in Keras
016 Pre-trained models
017 Recognize images with ResNet50 model



6. Monitoring a Keras model with TensorBoard
018 Export Keras logs in TensorFlow format
019 Visualize the computational graph
020 Visualize training progress



7. Using a Trained Keras Model in Google Cloud
021 Exporting Google Cloud-compatible models
022 Configuring a new Google Cloud account
023 Uploading a Keras model to Google Cloud
024 Using a model in Google Cloud



CONCLUSION
025 Next steps



Building and Deploying Deep Learning Applications with TensorFlow with Adam Geitgey

1h 46m • COURSE
TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.
Topics include:
  • What's TensorFlow?
  • Hardware, software, and language requirements
  • Creating a TensorFlow model
  • Training a deep learning model with TensorFlow
  • Visualizing the computational graph
  • Adding custom visualizations to TensorBoard
  • Exporting models for use with Google Cloud

1. Introduction
01 Welcome
02 What you should know
03 Using the exercise files



2.Setting Up TensorFlow
04. Install TensorFlow on macOS
05. Install TensorFlow on Windows



3.TensorFlow Overview
06. What is TensorFlow
07. Why is it called TensorFlow
08. Hardware, software, and language requirements
09. The train_test_evaluation flow in TensorFlow
10. Build a simple model in TensorFlow



4. Creating a TensorFlow Model
11. Options for loading data
12. Load the data set
13. Define the model structure
14. Set up the model training loop



5. Training a Model in TensorFlow
15. Train
16. Log
17. Save and load trained models



6. TensorBoard
18. Visualize the computational graph
19. Visualize training runs
20. Add custom visualizations to TensorBoard



7. Using a Trained TensorFlow
21. Export models for use in production
22. Configure a new Google Cloud account
23. Host your model in the cloud with Google Cloud
24. Use a model in the cloud



8. Conclusion
25. Next steps



Machine Learning & AI: Advanced Decision Trees with Keith McCormick

1h 16m • COURSE
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Topics include:
  • Understanding QUEST functions and applications
  • C5.0 concepts and practical applications
  • Understanding information gain
  • Random forests
  • Boosting and bagging
  • Costs and priors

 

1. Introduction
01_01 Welcome
01_02 What you should know
01_03 Using the exercise files



02. Decision Trees in IBM SPSS Modeler
02_01-Decision tree options in SPSS Modeler
02_02-Building a quick CHAID model
02_03-Adding a second model with CRT
02_04-Analysis nodes
02_05-Lift and gains chart



03. Understanding CHAID
03_01-What is an algorithm
03_02-Chisquared overview
03_03-Buliding a tree interactively
03_04-Bonferonni adjustment
03_05-What is level of measurement
03_06-How CHAID handles nominal variables
03_07-How CHAID handles ordinal variables
03_08-How CHAID handles continuous variables
03_09-A quick look at the complete CHAID tree



04. Understanding CRT
04_01-What is the Gini coefficient
04_02-How does CRT weigh purity and balance
04_03-How CRT handles nominal, ordinal, and continuous variables
04_04-How CRT handles missing data
04_05-Understanding pruning
04_06-A quick look at the complete CRT tree



05. Improving Your Model
05_01-Stopping rules in CHAID and CRT
05_02-Exhaustive CHAID
05_03-The Auto Classifier tuning trick



06. Conclusion
06_01-Next steps



Building a Recommendation System with Python Machine Learning & AI with Lillian Pierson, P.E.

1h 38m • COURSE
Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
Topics include:
  • Working with recommendation systems
  • Evaluating similarity based on correlation
  • Building a popularity-based recommender
  • Classification-based recommendations
  • Making a collaborative filtering system
  • Content-based recommender systems
  • Evaluating recommenders

1. Introduction
01. Welcome
02. Using the exercise files



2.Simple Approaches to Recommendation Systems
03. Introducing core concepts of recommendation systems
04. Popularity-based recommenders
05. Evaluating similarity based on correlation



3. Machine Learning Recommendation Systems
06. Classification-based collaborative filtering
07. Model-based collaborative filtering systems
08. Content-based recommender systems
09. Evaluating recommendation systems



4. Conclusion
10. Next steps



Amazon Web Services Machine Learning Essential Training with Lynn Langit

3h 7m • COURSE
Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. In this course, learn about patterns, services, processes, and best practices for designing and implementing machine learning using AWS. Instructor Lynn Langit takes a look at general machine learning concepts, including key machine learning algorithm types. She also examines available service types, such as AWS Machine Learning, Lex, Polly, and Rekognition, which you can use to predict image and video labels. Plus, she steps through how to work with platforms like AWS SageMaker, which includes hosted Jupyter notebooks.
Topics include:
  • How machine learning is used in analytics
  • AWS AI servers vs. platforms
  • Predicting using Polly text-to-speech
  • Predicting using Rekognition for video
  • Using Lex to build a conversational application
  • Using the AWS Machine Learning service to train, host, and predict
  • Working with MXNet in Databricks
  • Working with EMR for machine learning

1. Introduction
01. Welcome
02. About Cloud Services



2. Machine Learning on AWS
01. AWS Machine Learning Concepts
02. Business Scenarios for Machine Learning
03. Which Algorithm Should in Use
04. AWS AI Servers vs Platforms



05. AWS AI Platforms vs Frameworks
06. A Classifier in Action: Amazon Macie


3. Machine Learning API Services
01. Setup for AWS Machine Learning APIs
02. Predict using AWS Comprehend for NLP
03. Predict using AWS Polly text-to speech
04. Predict using AWS Lex for Chatbots



05. Predict Using AWS Rekognition for Images
06. Predict Using AWS Reckognition for Videose
07. Predict Using AWS Transcribe and Translate


4. Machine Learning Platforms
01. Understanding ML Platforms
02. Understanding and Using AWS Machine Learning
03. Understanding SageMaker
04. Create Jupyter Notebooks with Sage Maker



05. Get Data with SageMaker Notebook
06. Train Model with SageMaker job
07. Deploy and host model with SageMaker model
08. Use Model with SageMaker endpoint



09. Selecting algorithm for model training
10. Advanced use of SageMaker


5. Machine Learning Virtual Servers
01. Understanding ML Virtual Servers
02. Understanding Deep Learning
03. Work with Gluon for MXNet in SageMaker
04.Work with MXnet in Sage Maker



05. Databricks on AWS
06. Work with MXNet in Databricks
07. Setup the AWS Deep Learning AMIs
08. Work with AWS Deep Learning AMI



09. Work with EMR for Machine Learning


6. Machine Learning Architechtures
01. ÀWS ML APIs for Conversational apps
02. AWS Service for IOT Apps
03. Spark ML and Databricks for real-time apps
04. VariantSpark and EMR for genomic research



05. Best practices for algorithms and architectures


7. Next Steps
01. Next Steps