Part 1. The machine-learning workflow


In this first part of the book, we introduce the basic machine-learning workflow. Each chapter covers one step of the workflow.

Chapter 1 introduces machine learning, what it’s useful for, and why you should be reading this book.

In chapter 2, you’ll dive into the data-processing step of the basic ML workflow. You’ll look at common ways to clean up and extract value from real-world and messy data.

In chapter 3, you’ll start building simple ML models as you learn about a few modeling algorithms and how they’re used in common implementations.

In chapter 4, you’ll take a deeper look at our ML models to evaluate and optimize their performance.

Chapter 5 is dedicated to basic feature engineering. Extracting features from data can be an extremely important part of building and optimizing the performance of an ML system.