Book description
R gives you access to the cuttingedge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.
 Harness the power of R for statistical computing and data science
 Use R to apply common machine learning algorithms with realworld applications
 Prepare, examine, and visualize data for analysis
 Understand how to choose between machine learning models
 Packed with clear instructions to explore, forecast, and classify data
In Detail
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning wellsuited to the presentday era of "big data" and "data science". Given the growing prominence of R—a crossplatform, zerocost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.
"Machine Learning with R" is a practical tutorial that uses handson examples to step through realworld application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Wellsuited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.
How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to realworld problems, you will gain handson experience that will transform the way you think about data.
"Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
Table of contents

Machine Learning with R
 Table of Contents
 Machine Learning with R
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Introducing Machine Learning

2. Managing and Understanding Data
 R data structures
 Vectors
 Factors
 Managing data with R

Exploring and understanding data
 Exploring the structure of data

Exploring numeric variables
 Measuring the central tendency – mean and median
 Measuring spread – quartiles and the fivenumber summary
 Visualizing numeric variables – boxplots
 Visualizing numeric variables – histograms
 Understanding numeric data – uniform and normal distributions
 Measuring spread – variance and standard deviation
 Exploring categorical variables
 Exploring relationships between variables
 Summary
 3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes
 Understanding naive Bayes
 Example – filtering mobile phone spam with the naive Bayes algorithm
 Summary

5. Divide and Conquer – Classification Using Decision Trees and Rules
 Understanding decision trees
 Example – identifying risky bank loans using C5.0 decision trees
 Understanding classification rules
 Example – identifying poisonous mushrooms with rule learners
 Summary

6. Forecasting Numeric Data – Regression Methods
 Understanding regression
 Example – predicting medical expenses using linear regression
 Understanding regression trees and model trees
 Example – estimating the quality of wines with regression trees and model trees
 Summary
 7. Black Box Methods – Neural Networks and Support Vector Machines
 8. Finding Patterns – Market Basket Analysis Using Association Rules

9. Finding Groups of Data – Clustering with kmeans

Understanding clustering
 Clustering as a machine learning task
 The kmeans algorithm for clustering
 Finding teen market segments using kmeans clustering
 Step 1 – collecting data
 Step 2 – exploring and preparing the data
 Step 3 – training a model on the data
 Step 4 – evaluating model performance
 Step 5 – improving model performance
 Summary

Understanding clustering
 10. Evaluating Model Performance
 11. Improving Model Performance
 12. Specialized Machine Learning Topics
 Index
Product information
 Title: Machine Learning with R
 Author(s):
 Release date: October 2013
 Publisher(s): Packt Publishing
 ISBN: 9781782162148
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