Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Part I. The Fundamentals of Machine Learning

The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

What Is Machine Learning? 4

Why Use Machine Learning? 4

Types of Machine Learning Systems 8

Supervised/Unsupervised Learning 8

Batch and Online Learning 15

Instance-Based Versus Model-Based Learning 18

Main Challenges of Machine Learning 24

Insufficient Quantity of Training Data 24

Nonrepresentative Training Data 26

Poor-Quality Data 27

Irrelevant Features 27

Overfitting the Training Data 28

Underfitting the Training Data 30

Stepping Back 30

Testing and Validating 31

Hyperparameter Tuning and Model Selection 32

Data Mismatch 33

Exercises 34

End-to-End Machine Learning Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Working with Real Data 38

Look at the Big Picture 39

iii

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