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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 – this book will take you methodically through every stage of applying machine learning.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782162148
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
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

Chapter 12. Specialized Machine Learning Topics

By now, you are probably eager to start applying machine learning to your own projects—you may have even already done so. If you have attempted a project on your own, you likely found that, the task of turning data into action is more difficult than this book made it appear.

As you attempted to gather data, you might have realized that the information was trapped in a proprietary spreadsheet format or spread across pages on the Web. Making matters worse, after spending hours manually reformatting the data, perhaps your computer slowed to a crawl after running out of memory. Perhaps R even crashed or froze your machine. Hopefully you were undeterred; it does get easier with time.

This chapter covers techniques that may not apply to every machine learning project, but could prove useful for certain types of work. You might find the information particularly useful if you tend to work with data that are:

  • Stored in unstructured or proprietary formats...

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