by Asli Bilgin | Publisher: Addison-Wesley Professional | Release Date: September 2019 | ISBN: 9780135945131

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1. Introduction
   • Machine Learning Fundamentals with Amazon SageMaker on AWS: Introduction 00:09:34
2. Module 1: What is Amazon SageMaker?
   • Module introduction 00:01:01
3. Lesson 1: Amazon Artificial Intelligence and Machine Learning Overview
   • Learning objectives 00:01:06
   • 1.1 Evolution of Artificial Intelligence (AI) and Machine Learning (ML) 00:05:18
   • 1.2 What is ML? 00:09:30
   • 1.3 AWS ML & AI: Platform Services 00:05:40
   • 1.4 AWS ML & AI: Application Services 00:05:30
   • 1.5 AWS ML & AI: Foundational Services 00:00:44
   • 1.6 Sample AI/ML Case Studies with AWS 00:04:45
4. Lesson 2: How Does Amazon SageMaker Work?
   • Learning objectives 00:00:35
   • 2.1 What is Amazon SageMaker? 00:02:50
   • 2.2 Who Should use Amazon SageMaker? 00:00:45
   • 2.3 What are the Benefits of Amazon SageMaker? 00:01:38
   • 2.4 High Level Overview 00:02:09
   • 2.5 Options for Data Sources 00:07:45
   • 2.6 Supervised Machine Learning 00:04:08
   • 2.7 Unsupervised Machine Learning 00:02:22
   • 2.8 Life Cycle of ML Processing 00:04:14
5. Lesson 3: Which Use Cases Can Amazon SageMaker Solve?
   • Learning objectives 00:00:38
   • 3.1 Personalization 00:03:12
   • 3.2 Search 00:01:34
   • 3.3 Marketing 00:01:42
   • 3.4 Finance 00:02:50
   • 3.5 Personal Productivity 00:03:47
   • 3.6 Product Management 00:04:21
6. Lesson 4: High Level Overview of the Amazon SageMaker Components
   • Learning objectives 00:00:36
   • 4.1 Components of Amazon SageMaker 00:01:54
   • 4.2 Demo: AWS SageMaker Console 00:01:59
   • 4.3 Amazon SageMaker Notebooks Service 00:01:31
   • 4.4 Amazon SageMaker Training Service 00:01:45
   • 4.5 Amazon SageMaker Hosting Service 00:00:56
   • 4.6 Pricing 00:05:22
7. Module 2: Fundamental Machine Learning Concepts with Practical Applications
   • Module introduction 00:00:38
8. Lesson 5: Machine Learning Concepts and Taxonomy
   • Learning objectives 00:00:25
   • 5.1 What is Input Data? 00:04:57
   • 5.2 What are Features? 00:03:38
   • 5.3 What is a Target? 00:02:39
   • 5.4 What are Observations? 00:02:23
   • 5.5 What is Labeled Data? 00:01:42
   • 5.6 What is Unlabeled Data? 00:01:37
   • 5.7 What is Ground Truth? 00:01:38
   • 5.8 What are Hyperparameters? 00:00:58
   • 5.9 What are Predictions or Inferences 00:00:49
9. Lesson 6: Selecting the Appropriate Data
   • Learning objectives 00:00:24
   • 6.1 What is the Best Kind of Data? 00:07:43
   • 6.2 Academic and Commercial Sources for Data 00:05:16
   • 6.3 Sample Business Problem for This Course 00:04:40
10. Lesson 7: Practical Applications for Machine Learning
    • Learning objectives 00:00:36
    • 7.1 How to Frame a Suitable Problem 00:01:57
    • 7.2 Scenarios 00:03:49
    • 7.3 Curator Project Sample Business Problem 00:01:58
    • 7.4 Best Practices for Selecting a Business Problem 00:01:40
11. Module 3: Amazon SageMaker Supporting Tools and Technologies
    • Module introduction 00:00:45
12. Lesson 8: Refresher on Technologies Leveraged by Amazon SageMaker
    • Learning objectives 00:00:32
    • 8.1 Amazon S3 00:02:10
    • 8.2 EC2 Instances 00:01:18
    • 8.3 Identity Access Management (IAM) 00:01:21
    • 8.4 Taxonomy 00:01:26
    • 8.5 Key Packages and Libraries 00:05:58
    • 8.6 Package Management and SDKs 00:06:06
13. Lesson 9: Interactive Lab: Review the SageMaker Console
    • Learning objectives 00:00:31
    • 9.1 Login to AWS Console 00:02:40
    • 9.2 Amazon SageMaker Dashboard Walkthrough 00:04:01
14. Lesson 10: Interactive Lab: Working with Jupyter Notebooks
    • Learning objectives 00:00:35
    • 10.1 Components of Jupyter Notebooks 00:05:29
    • 10.2 Jupyter Notebooks and Amazon SageMaker 00:02:37
    • 10.3 Tips for Code Execution 00:01:19
    • 10.4 Demo: Create a Notebook Instance 00:13:28
15. Lesson 11: Interactive Lab: Example SageMaker Notebooks
    • Learning objectives 00:00:37
    • 11.1 High Level Overview 00:03:26
    • 11.2 Common Algorithms with Amazon SageMaker Samples 00:09:01
    • 11.3 How to Bring Your Own Algorithm 00:01:40
    • 11.4 Interactive Lab: Review Amazon SageMaker Pre-built Notebook Types 00:04:27
    • 11.5 Interactive Lab: Walkthrough of a Pre-built Sample Jupyter Notebook 00:09:22
16. Module 4 Data and Model Management with Amazon SageMaker
    • Module introduction 00:01:27
17. Lesson 12: Interactive Lab: Working with Jupyter Notebooks
    • Learning objectives 00:00:39
    • 12.1 Best Practices for Scrubbing Data 00:04:14
    • 12.2 Best Practices for Handling Missing Values 00:03:13
    • 12.3 Demo: Source Data from SQL Server 00:08:53
    • 12.4 Demo: Feature Selection 00:02:48
    • 12.5 Interactive Lab: Create an S3 Bucket and Folder 00:01:48
    • 12.6 Interactive Lab: Upload Data through AWS Console 00:01:11
    • 12.7 Interactive Lab: Visualization Demo 00:07:44
18. Lesson 13: A Closer Look at Algorithms
    • Learning objectives 00:00:40
    • 13.1 How to Approach Learning ML Algorithms 00:03:03
    • 13.2 Sample Popular Algorithms 00:08:36
    • 13.3 Choosing an Algorithm 00:00:43
    • 13.4 Interactive Lab: Demo of a Popular Algorithm 00:09:47
19. Lesson 14: Algorithm Selection
    • Learning objectives 00:00:34
    • 14.1 Choose the Appropriate Algorithm 00:01:24
    • 14.2 Create Features and Labels 00:03:56
    • 14.3 Split Data - Training, Validation, Test 00:03:26
20. Lesson 15: Model Training
    • Learning objectives 00:01:18
    • 15.1 Overview of Model Training with Amazon SageMaker 00:02:05
    • 15.2 Model Training Workflow 00:01:36
    • 15.3 Model Training and Evaluation Tips 00:01:52
    • 15.4 Running High Compute Taining Jobs with EC2 Instances 00:01:29
21. Lesson 16: Assess Model Performance
    • Learning objectives 00:00:42
    • 16.1 How to Evaluate Model Performance 00:01:32
    • 16.2 Sample Metrics for Assessing Accuracy Using Common Models 00:02:48
    • 16.3 Subjective Analysis 00:01:32
    • 16.4 Objective Analysis Using Tools 00:01:48
    • 16.5 Common Causes of Poor Performance 00:04:29
    • 16.6 How to Refine Your Model 00:01:23
22. Module 5 Predictions and Deployment with Amazon SageMaker
    • Module introduction 00:00:52
23. Lesson 17: Deploy Model
    • Learning objectives 00:00:22
    • 17.1 Overview Amazon SageMaker Hosting Services 00:03:30
    • 17.2 How to Create an Endpoint with Elastic Inference 00:01:53
    • 17.3 Deploy Model 00:00:49
    • 17.4 Demo: Curator 00:05:09
24. Lesson 18: Predictions or Inferences
    • Learning objectives 00:01:21
    • 18.1 How do Predictions Work? 00:01:27
    • 18.2 What are the Types of Predictions? 00:03:12
    • 18.3 Generate Real Time Predictions 00:01:10
    • 18.4 Batch Predictions 00:01:14
    • 18.5 Cleanup 00:01:28
25. Lesson 19: Call to Action & Conclusion
    • Learning objectives 00:00:43
    • 19.1 Final Review 00:02:03
    • 19.2 Next Steps 00:01:54
    • 19.3 References 00:01:29
26. Summary
    • Machine Learning Fundamentals with Amazon SageMaker on AWS: Summary 00:00:52



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