Welcome

Thank you for purchasing the MEAP for Machine Learning with R, tidyverse, and mlr! To get the most from this book, you should have basic R programming skills such as working with functions, objects, and data, and some very basic statistical knowledge.

During my PhD, I found that traditional statistical modeling approaches were not always sufficient for the types of data problems I was tackling. As the number of variables and complexity of the questions began to increase, I turned to machine learning techniques to extract meaningful predictions from my data instead. Working in academia, R was my tool of choice, and it has certainly come-of-age for machine learning applications with packages such as caret and mlr.

In this book you'll learn the basics of machine learning, and how many commonly used machine learning techniques work and how to apply them to your data. You'll learn all of this while using the mlr package in R, a modern and extremely flexible package that will simplify your learning process and get you building your own machine learning pipelines quickly. As building well-performing machine learning pipelines is about more than just training models, the book also incorporates and teaches tools from the tidyverse collection of packages, that help you transform, clean and plot your data ready for analysis. In fact, I devote an entire chapter to these tools near the start of the book, and use them in the code examples throughout the rest of the book.