Book Image

Machine Learning with R Cookbook - Second Edition

By : AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook - Second Edition

By: AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)

Overview of this book

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Table of Contents (21 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback

Chapter 1. Practical Machine Learning with R

In this chapter, we will cover the following topics:

  • Downloading and installing R
  • Downloading and installing RStudio
  • Installing and loading packages
  • Understanding basic data structures
  • Basic commands for subsetting
  • Reading and writing data
  • Manipulating data
  • Applying basic statistics
  • Visualizing data
  • Getting a dataset for machine learning