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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

Improved recommendations


Remember where we stopped in the previous chapter: with a very basic, but not very good, recommendation system that gave better than random predictions. We are now going to start improving it. First, we will go through a couple of ideas that will capture some part of the problem. Then, what we will do is combine multiple approaches rather than using a single approach in order to be able to achieve a better final performance.

We will be using the same movie recommendation dataset that we started off with in the last chapter; it consists of a matrix with users on one axis and movies on the other. It is a sparse matrix, as each user has only reviewed a small fraction of the movies.

Using the binary matrix of recommendations

One of the interesting conclusions from the Netflix Challenge was one of those obvious-in-hindsight ideas: we can learn a lot about you just from knowing which movies you rated, even without looking at which rating was given! Even with a binary matrix...

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