Introduction to Machine Learning with R [Early Release]

Machine learning can be a difficult if you’ve not familiar with basics of the concept. With this book, you get a solid foundation of introductory principles used in machine learning with R. If you’re familiar with R, you’ll start with the basics like regression, then move into more advanced topics like Neural Networks, and finally into the frontier of machine learning in the R world with packages like Caret.

By developing a vocabulary of machine learning topics and understanding the difference between a clustering algorithm and a regression, you’ll be able to solve an array of problems. Knowing when to use a regression model and when not to use one can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples throughout.

Understand the major parts of machine learning algorithms
Recognize how machine learning can be used to solve a problem in a simple manner
Figure out when to use certain machine learning algorithms versus others
Learn how to operationalize algorithms with cutting edge packages