Mastering Machine Learning with R - Second Edition

Master machine learning techniques with R to deliver insights in complex projects

Mastering Machine Learning with R - Second Edition

Cory Lesmeister

Master machine learning techniques with R to deliver insights in complex projects
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Book Details

ISBN 139781787287471
Paperback420 pages

Book Description

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.

You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.

With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

Table of Contents

Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Chapter 6: Classification and Regression Trees
Chapter 16: Sources

What You Will Learn

  • Gain deep insights into the application of machine learning tools in the industry
  • Manipulate data in R efficiently to prepare it for analysis
  • Master the skill of recognizing techniques for effective visualization of data
  • Understand why and how to create test and training data sets for analysis
  • Master fundamental learning methods such as linear and logistic regression
  • Comprehend advanced learning methods such as support vector machines
  • Learn how to use R in a cloud service such as Amazon

Authors

Table of Contents

Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Chapter 6: Classification and Regression Trees
Chapter 16: Sources

Book Details

ISBN 139781787287471
Paperback420 pages
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