Overview

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.

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9781138495685

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Reviews

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

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 ...The book describes in detail the various methods for solving classification and clustering problems. Functions from many R libraries are compared, which enables the reader to understand their respective advantages and disadvantages. The authors have developed a clear structure to the book that includes a brief description of each model, examples of using the model for specific real-life examples, and discussion of the advantages and disadvantages of the model. This structure is one of the book's main advantages. - Igor Malyk, ISCB News, July 2020

Author Information

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.

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