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.


·         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.

part Part I|76 pages


chapter 1|10 pages

Introduction to Machine Learning

chapter 2|28 pages

Modeling Process

chapter 3|36 pages

Feature & Target Engineering

part Part II|266 pages

Supervised Learning

chapter 4|26 pages

Linear Regression

chapter 5|16 pages

Logistic Regression

chapter 6|20 pages

Regularized Regression

chapter 7|16 pages

Multivariate Adaptive Regression Splines

chapter 8|17 pages

K-Nearest Neighbors

chapter 9|16 pages

Decision Trees

chapter 10|13 pages


chapter 11|17 pages

Random Forests

chapter 12|26 pages

Gradient Boosting

chapter 13|24 pages

Deep Learning

chapter 14|20 pages

Support Vector Machines

chapter 15|14 pages

Stacked Models

chapter 16|38 pages

Interpretable Machine Learning

part Part III|53 pages

Dimension Reduction

chapter 17|14 pages

Principal Components Analysis

chapter 18|18 pages

Generalized Low Rank Models

chapter 19|19 pages


part Part IV|45 pages


chapter 20|18 pages

K-means Clustering

chapter 21|12 pages

Hierarchical Clustering

chapter 22|13 pages

Model-based Clustering