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Python Machine Learning Blueprints

You're reading from   Python Machine Learning Blueprints Put your machine learning concepts to the test by developing real-world smart projects

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788994170
Length 378 pages
Edition 2nd Edition
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Authors (3):
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Michael Roman Michael Roman
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Michael Roman
Alexander Combs Alexander Combs
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Alexander Combs
Saurabh Chhajed Saurabh Chhajed
Author Profile Icon Saurabh Chhajed
Saurabh Chhajed
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Table of Contents (13) Chapters Close

Preface 1. The Python Machine Learning Ecosystem FREE CHAPTER 2. Build an App to Find Underpriced Apartments 3. Build an App to Find Cheap Airfares 4. Forecast the IPO Market Using Logistic Regression 5. Create a Custom Newsfeed 6. Predict whether Your Content Will Go Viral 7. Use Machine Learning to Forecast the Stock Market 8. Classifying Images with Convolutional Neural Networks 9. Building a Chatbot 10. Build a Recommendation Engine 11. What's Next? 12. Other Books You May Enjoy

Hybrid systems

We've now looked at the two primary forms of recommender systems, but you should know that, in any large-scale production environment, you're likely to see recommendations that leverage both of these. This is known as a hybrid system, and the reason hybrid systems are preferred is that they help eliminate the drawbacks that can be present when using either system alone. The two systems together create a more robust solution.

Let's examine the pros and cons of each type.

Collaborative filtering

The pros of collaborative filtering are as follows:

  • There's no need to hand-craft features

The cons are as follows:

  • Doesn't work well without a large number of items and users
  • Sparsity when...
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