Hands-On Machine Learning with R

The R Series

by Brad Boehmke, Brandon M. Greenwell

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. 

Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Features:

·         Offers a practical and applied introduction to the most popular machine learning methods.

·         Topics covered include feature engineering, resampling, deep learning and more.

·         Uses a hands-on approach and real world data.

488 pages, Kindle Edition

Published November 7, 2019

Reviews

[O · Rating 5 out of 5]

This is my first feedback ever. And I am writing it because of this book. A superb book. Probably my favorite ML book so far. Or at least top-3. Authors did a great job, explaining key concepts in the intuitive format.
This book is not free and is quite expensive. A digital copy will cost you 70 bucks. But trust me, it is worth of it.
Add here the code https://koalaverse.github.io/homlr/
and some book chapters https://bradleyboehmke.github.io/HOML/
I am read this book every week again and again. If you code in R, it is gem. If you do Python or something else, still great theory explanations.
I strongly suggest you to buy it, if you learn ML.

[Auggie Heschmeyer · Rating 5 out of 5]

As someone who learned data science through coding rather than a math textbook, I love when machine learning algorithms are explained with code rather than mathematical notation. As long as you have some background with R, this book teaches all of the fundamentals to be a functional machine learning programmer. This book is up there with Hadpey Wickham and Garret Groleman's "R for Data Science" as an absolute essential for people interested in R.

And God bless Mr. Boehmke for making this book free to all.

[Wej · Rating 4 out of 5]

Very good introduction to ML algorithms using R. What's even better is that it's freely available online. The book is divided into four main parts: fundamentals, supervised learning, dimension reduction, and clustering. Each part contains short chapters briefly describing the key algorithms and showing how they can be used. Algorithms are described succintly using mathematical notations, but the majority of explanations is given in text, code, and figures. The supervised algorithms are mostly implemented in h2o. Sometimes, it felt like the author tried very hard to avoid using caret, even though in many cases it was the best tool for the job. Some more exotic packages are also demonstrated. The examples are written using tidyverse packages.

[Martine · Rating 3 out of 5]

A lot of useful topics with indeed hands-on R code. The author told us in our boook club meet that the new edition is almost available. So I would wait for the new edition (hopefully they mention the used seeds for the calculations :) ), in the mean while you could start reading the online first edition via https://bradleyboehmke.github.io/HOML/. The code for the algorithms and the code for the plots is also available. Nice and helpful.