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Machine Learning with R

You're reading from Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

Product type Paperback

Published in Jul 2015

Publisher Packt

ISBN-13 9781784393908

Length 452 pages

Edition 2nd Edition

Languages

R

Concepts

Machine Learning

Author (1):

Brett Lantz

View More author details

Table of Contents (14) Chapters

Preface

1. Introducing Machine Learning

2. Managing and Understanding Data FREE CHAPTER

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

What you need for this book

The examples in this book were written for and tested with R version 3.2.0 on Microsoft Windows and Mac OS X, though they are likely to work with any recent version of R.

The rest of the chapter is locked

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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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