I firmly believe that machine learning should not be the domain only of computer scientists and people with degrees in mathematics. Machine learning with R, the tidyverse, and mlr doesn’t assume you come from either of these backgrounds. To get the most from the book, though, you should be reasonably familiar with the R language. It will help if you understand some basic statistical concepts, but all that you’ll need is included as a statistics refresher in the appendix, so head there first to fill in any gaps in your knowledge. Anyone with a problem to solve, and data that contains the answer to that problem, can benefit from the topics taught in this book.
If you are a newcomer to R and want to learn or brush up on your basic R skills, I suggest you take a look at R in Action, by Robert I. Kabacoff (Manning, 2015).
This book has 5 parts, covering 20 chapters. The first part of the book is designed to get you up and running with some of the broad machine learning and R skills you’ll use throughout the rest of the book. The first chapter is designed to get your machine learning vocabulary up to speed. The second chapter will teach you a large number of tidyverse functions that will improve your general R data science skills.