Hands-On Machine Learning with R

Brad Boehmke

ISBN 10: 1138495689 / ISBN 13: 9781138495685

Published by Chapman And Hall/CRC, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book is designed to introduce the concept of advanced business analytic approaches and would the first to cover the gamut of how to use the R programming language to apply descriptive, predictive, and prescriptive analytic methodologies for problem solving.

Seller Inventory # 9781138495685

Bibliographic Details

• Title: Hands-On Machine Learning with R
• Publisher: Chapman And Hall/CRC
• Publication Date: 2019
• Language: English
• ISBN 10: 1138495689
• ISBN 13: 9781138495685
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 30D millimeters 240 millimeters width by 161 millimeters height by 30 millimeters depth
• Weight: 886 grams

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.

About the Author

Brad Boehmke is a data scientist at 84.51° where he wears both software developer and machine learning engineer hats. He is an Adjunct Professor at the University of Cincinnati, author of Data Wrangling with R, and creator of multiple public and private enterprise R packages.

Brandon Greenwell is a data scientist at 84.51° where he works on a diverse team to enable, empower, and encourage others to successfully apply machine learning to solve real business problems. He’s part of the Adjunct Graduate Faculty at Wright State University, an Adjunct Instructor at the University of Cincinnati, and the author of several R packages available on CRAN.