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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher
ISBN-13 9781788623223
Length 406 pages
Edition 3rd Edition
Languages
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Authors (3):
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Luis Pedro Coelho Luis Pedro Coelho
Author Profile Icon Luis Pedro Coelho
Luis Pedro Coelho
Willi Richert Willi Richert
Author Profile Icon Willi Richert
Willi Richert
Matthieu Brucher Matthieu Brucher
Author Profile Icon Matthieu Brucher
Matthieu Brucher
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Machine Learning 2. Classifying with Real-World Examples FREE CHAPTER 3. Regression 4. Classification I – Detecting Poor Answers 5. Dimensionality Reduction 6. Clustering – Finding Related Posts 7. Recommendations 8. Artificial Neural Networks and Deep Learning 9. Classification II – Sentiment Analysis 10. Topic Modeling 11. Classification III – Music Genre Classification 12. Computer Vision 13. Reinforcement Learning 14. Bigger Data 15. Where to Learn More About Machine Learning 16. Other Books You May Enjoy

Recommendations

Recommendations have become one of the staples of online services and commerce. This type of automated system can provide each user with a personalized list of suggestions (be it a list of products to purchase, features to use, or new connections). In this chapter, we will see the basic ways in which automated recommendation generation systems work. The field of generating recommendations based on consumer input is often called collaborative filtering, as the users collaborate through the system to find the best items for each other.

In the first part of this chapter, we will see how we can use past product ratings from consumers to predict new ratings. We start with a few ideas that are helpful and then combine all of them. When combining them, we use regression to learn the best way in which they can be combined. This will also allow us to explore a generic concept...

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