Hands-On Machine Learning with R - 1st ed

Boehmke, Brad; Greenwell, Brandon M.

ISBN 10: 1138495689 / ISBN 13: 9781138495685

Published by Chapman and Hall/CRC, 2019

About this Item

This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Seller Inventory # ABTA-124722

Bibliographic Details

Title: Hands-On Machine Learning with R - 1st ed

Publisher: Chapman and Hall/CRC

Publication Date: 2019

Binding: Hardcover

Condition: New

About this title

Synopsis:

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.

Review:

"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages."
- Max Kuhn, Machine Learning Software Engineer, RStudio

"You can't find a better overview of practical machine learning methods implemented with R."
- JD Long, co-author of R Cookbook

"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner."
- Mara Averick, RStudio Dev Advocate