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Machine Learning with R

You're reading from Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Product type Paperback

Published in Oct 2013

Publisher Packt

ISBN-13 9781782162148

Length 396 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Brett Lantz

View More author details

Table of Contents (19) Chapters

Machine Learning with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

1. Introducing Machine Learning FREE CHAPTER

2. Managing and Understanding Data

3. Lazy Learning – Classification Using Nearest Neighbors

4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules

6. Forecasting Numeric Data – Regression Methods

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 k-means

10. Evaluating Model Performance

11. Improving Model Performance

12. Specialized Machine Learning Topics

Index

R data structures

There are numerous types of data structures across programming languages, each with strengths and weaknesses specific to particular tasks. Since R is a programming language used widely for statistical data analysis, the data structures it utilizes are designed to make it easy to manipulate data for this type of work. The R data structures used most frequently in machine learning are vectors, factors, lists, arrays, and data frames. Each of these data types is specialized for a specific data management task, which makes it important to understand how they will interact in your R project.

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Authors (1)

Brett Lantz

Brett Lantz

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

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