Introduction to Machine Learning with R: Rigorous Mathematical Analysis 1st Edition

Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks and finally delve into the frontier of machine learning in the R world with packages like Caret.

By developing a familiarity topics with like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or can not mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning.