The first three chapters of the book provide you an introduction of some basic concepts and models of machine learning to help you understand the basic machine learning building blocks and lay the groundwork for learning AutoML. You’ll start to get a glimpse of AutoML, its concepts, and its connection with general machine learning in chapter 1. You’ll learn the research value and practical benefits of AutoML. Chapter 2 introduces the classic machine learning pipeline for addressing a machine learning problem. Considering the popularity of deep learning models in the AI community and beyond, in chapter 3, we cover the basic knowledge of deep learning with the examples of three popular types of models. We do not touch the complicated deep learning concepts but just introduce the basic building blocks and three classical models applied on different data formats. If you don’t have much experience with machine learning, deep learning, and how to apply them with Python, you should definitely begin by reading part 1 in full before moving on to the practical applications of AutoML in part 2. You can also find additional examples in appendix B to become more familiar with the basic machine learning pipeline after reading this part of the book.