Table of Contents
Preface
Section 1: Introduction to Machine Learning
Chapter 1: Machine Learning Fundamentals
Comparing AI, ML, and DL
Examining ML
Examining DL
Classifying supervised, unsupervised, and reinforcement learning
Introducing supervised learning
The CRISP-DM modeling life cycle
Data splitting
Overfitting and underfitting
Applying cross-validation and measuring overfitting
Bootstrapping methods
The variance versus bias trade-off
Shuffling your training set
Modeling expectations
Introducing ML frameworks
ML in the cloud
Summary
Questions
Chapter 2: AWS Application Services for AI/ML
Technical requirements
Analyzing images and videos with Amazon Rekognition
Exploring the benefits of Amazon Rekognition
Getting hands-on with Amazon ...
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