While this first part of the book includes only two chapters, it is essential to provide you with the basic knowledge and skills you’ll rely on throughout the book.
Chapter 1 introduces you to some basic machine learning terminology. Having a good vocabulary for the core concepts can help you see the big picture of machine learning and aid in your understanding of the more complex topics we’ll explore later in the book. This chapter teaches you what machine learning is, how it can benefit (or harm) us, and how we can categorize different types of machine learning tasks. The chapter finishes by explaining why we’re using R for machine learning, what datasets you’ll be working with, and what you can expect to learn from the book.
In chapter 2, we take a brief detour away from machine learning and focus on developing your R skills by covering a collection of packages known as the tidyverse. The packages of the tidyverse provide us with the tools to store, manipulate, transform, and visualize our data using more human-readable, intuitive code. You don’t need to use the tidyverse when working on machine learning projects, but doing so helps you simplify your data-wrangling processes. We’ll use tidyverse tools in the projects throughout the book, so a solid grounding in them in chapter 2 can help you in the rest of the chapters. I’m sure you’ll find that these skills improve your general R programming and data science skills.