Lantz - Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications

Brett Lantz has spent the past 10 years using innovative data methods to understand human behavior. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When he's not spending time with family, following college sports, or being entertained by his dachshunds, he maintains dataspelunking.com, a website dedicated to sharing knowledge about the search for insight in data.

This book could not have been written without the support of my family and friends. In particular, my wife Jessica deserves many thanks for her patience and encouragement throughout the past year. My son Will (who was born while was underway), also deserves special mention for his role in the writing process; without his gracious ability to sleep through the night, I could not have strung together a coherent sentence the next morning. I dedicate this book to him in the hope that one day he is inspired to follow his curiosity wherever it may lead.

I am also indebted to many others who supported this book indirectly. My interactions with educators, peers, and collaborators at the University of Michigan, the University of Notre Dame, and the University of Central Florida seeded many of the ideas I attempted to express in the text. Additionally, without the work of researchers who shared their expertise in publications, lectures, and source code, this book might not exist at all. Finally, I appreciate the efforts of the R team and all those who have contributed to R packages, whose work ultimately brought machine learning to the masses.

Jia Liu holds a Master's degree in Statistics from the University of Maryland, Baltimore County, and is presently a PhD candidate in statistics from Iowa State University. Her research interests include mixed-effects model, Bayesian method, Boostrap method, reliability, design of experiments, machine learning and data mining. She has two year's experience as a student consultant in statistics and two year's internship experience in agriculture and pharmaceutical industry.

Mzabalazo Z. Ngwenya has worked extensively in the field of statistical consulting and currently works as a biometrician. He holds an MSc in Mathematical Statistics from the University of Cape Town and is at present studying for a PhD (at the School of Information Technology, University of Pretoria), in the field of Computational Intelligence. His research interests include statistical computing, machine learning, and spatial statistics. Previously, he was involved in reviewing Learning RStudio for R Statistical Computing ( Van de Loo and de Jong , 2012), and R Statistical Application Development by Example beginner's guide ( Prabhanjan Narayanachar Tattar , 2013).

Abhinav Upadhyay finished his Bachelor's degree in 2011 with a major in Information Technology. His main areas of interest include machine learning and information retrieval.

In 2011, he worked for the NetBSD Foundation as part of the Google Summer of Code program. During that period, he wrote a search engine for Unix manual pages. This project resulted in a new implementation of the apropos utility for NetBSD.

Currently, he is working as a Development Engineer for SocialTwist. His day-to-day work involves writing system level tools and frameworks to manage the product infrastructure.

He is also an open source enthusiast and quite active in the community. In his free time, he maintains and contributes to several open source projects.

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Preface

Machine learning, at its core, is concerned with algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present day era of Big Data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information.

Given the growing prominence of Ra cross-platform, zero-cost statistical programming environmentthere has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding data insights.