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Machine Learning with R – Second Edition, Second Edition

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Machine Learning with R - Second Edition

This is the code repository for Machine Learning with R - Second Edition, published by Packt. Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R

What is this book about?

Harness the power of R to build common machine learning algorithms with real-world data science applications
Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Classify your data with Bayesian and nearest neighbor methods
Predict values by using R to build decision trees, rules, and support vector machines Forecast numeric values with linear regression, and model your data with neural networks Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

subject_name, temperature, flu_status, gender, blood_type John Doe, 98.1, FALSE, MALE, O Jane Doe, 98.6, FALSE, FEMALE, AB Steve Graves, 101.4, TRUE, MALE, A

Following is what you need for this book: This book is intended for anybody hoping to use data for action. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little about R, but are new to machine learning. In any case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic math and programming concepts, but no prior experience is required. All you need is curiosity.

Get to Know the Author

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