This book is organized roughly as follows:
• Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup we will be using throughout the book.
• Chapters 2 and 3 describe the actual machine learning algorithms that are most widely used in practice, and discuss their advantages and shortcomings.
• Chapter 4 discusses the importance of how we represent data that is processed by machine learning, and what aspects of the data to pay attention to.
• Chapter 5 covers advanced methods for model evaluation and parameter tuning, with a particular focus on cross-validation and grid search.
• Chapter 6 explains the concept of pipelines for chaining models and encapsulating your workflow.
• Chapter 7 shows how to apply the methods described in earlier chapters to textdata, and introduces some text-specific processing techniques.
• Chapter 8 offers a high-level overview, and includes references to more advanced topics.