Machine Learning With R Cookbook Front Cover

Machine Learning With R Cookbook

  • Length: 405 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2015-03-31
  • ISBN-10: 1783982047
  • ISBN-13: 9781783982042
  • Sales Rank: #496141 (See Top 100 Books)

Explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code

About This Book

  • Apply R to simplify predictive modeling with short and simple code
  • Use machine learning to solve problems ranging from small to big data
  • Build a training and testing dataset from the churn dataset,applying different classification methods.

Who This Book Is For

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

In Detail

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.

This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

Table of Contents

Chapter 1. Practical Machine Learning with R
Chapter 2. Data Exploration with RMS Titanic
Chapter 3. R and Statistics
Chapter 4. Understanding Regression Analysis
Chapter 5. Classification (I) – Tree, Lazy, and Probabilistic
Chapter 6. Classification (II) – Neural Network and SVM
Chapter 7. Model Evaluation
Chapter 8. Ensemble Learning
Chapter 9. Clustering
Chapter 10. Association Analysis and Sequence Mining
Chapter 11. Dimension Reduction
Chapter 12. Big Data Analysis (R and Hadoop)

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