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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787287471

Pages 420 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Table of Contents (23) Chapters

Title Page

Credits

About the Author

About the Reviewers

Packt Upsell

Customer Feedback

Preface

1. A Process for Success

2. Linear Regression - The Blocking and Tackling of Machine Learning

3. Logistic Regression and Discriminant Analysis

4. Advanced Feature Selection in Linear Models

5. More Classification Techniques - K-Nearest Neighbors and Support Vector Machines

6. Classification and Regression Trees

7. Neural Networks and Deep Learning

8. Cluster Analysis

9. Principal Components Analysis

10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis

11. Creating Ensembles and Multiclass Classification

12. Time Series and Causality

13. Text Mining

14. R on the Cloud

15. R Fundamentals

16. Sources

Text mining framework and methods

There are many different methods to use in text mining. The goal here is to provide a basic framework to apply to such an endeavor. This framework is not all-inclusive of the possible methods but will cover those that are probably the most important for the vast majority of projects that you will work on. Additionally, I will discuss the modeling methods in as succinct and clear a manner as possible, because they can get quite complicated. Gathering and compiling text data is a topic that could take up several chapters. Therefore, let's begin with the assumption that our data is available from Twitter, a customer call center, scraped off the web, or whatever, and is contained in some sort of text file or files.

The first task is to put the text files in one structured file referred to as a corpus. The number of documents could be just one, dozens, hundreds, or even thousands. R can handle a number of raw text files, including RSS feeds, PDF files, and MS...

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