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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Basket analysis


The methods we have discussed so far work well when you have numeric ratings of how much a user liked a product. This type of information is not always available.

Basket analysis is an alternative mode of learning recommendations. In this mode, our data consists only of what items were bought together; it does not contain any information on whether individual items were enjoyed or not. It is often easier to get this data rather than ratings data as many users will not provide ratings, while the basket data is generated as a side effect of shopping. The following screenshot shows you a snippet of Amazon.com's web page for the book War and Peace, Leo Tolstoy, which is a classic way to use these results:

This mode of learning is not only applicable to actual shopping baskets, naturally. It is applicable in any setting where you have groups of objects together and need to recommend another. For example, recommending additional recipients to a user writing an e-mail is done by...

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