Brett Lantz - Machine Learning with R: Expert techniques for predictive modeling

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Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at 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. In particular, my wife Jessica deserves many thanks for her endless patience and encouragement. My sons Will and Cal were born in the midst of the first and second editions, respectively, and supplied much-needed diversions while writing this edition. I dedicate this book to them in the hope that one day they are inspired to tackle big challenges and follow their 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; any lack of clarity in their expression is purely mine. Additionally, without the work of the broader community 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 and RStudio teams and all those who have contributed to R packages, whose work have helped bring machine learning to the masses. I sincerely hope that my work is likewise a valuable piece in this mosaic.

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 the insights in your own data.

By combining hands-on case studies with the essential theory that you need to understand how things work under the hood, this book provides all the knowledge needed to start getting to work with machine learning.