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Building Recommendation Engines in Python

Learn to build recommendation engines in Python using machine learning techniques.

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4 Hours16 Videos60 Exercises3,783 Learners
4850 XP

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Course Description

We’ve come to expect personalized experiences online—whether it’s Netflix recommending a show or an online retailer suggesting items you might also like to purchase. But how are these suggestions generated? In this course, you’ll learn everything you need to know to create your own recommendation engine. Through hands-on exercises, you’ll get to grips with the two most common systems, collaborative filtering and content-based filtering. Next, you’ll learn how to measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE). By the end of this course, you’ll have built your very own movie recommendation engine and be able to apply your Python skills to create these systems for any industry.

  1. 1

    Introduction to Recommendation Engines


    What problems are recommendation engines designed to solve and what data are best suited for them? Discern what insightful recommendations can be made even with limited data, and learn how to create your own recommendations.

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    What are recommendation engines?
    50 xp
    Recommendation engines vs. predictions
    100 xp
    Identifying the correct data for recommendation engines
    50 xp
    Implicit vs. explicit data
    100 xp
    Non-personalized recommendations
    50 xp
    Introduction to non-personalized recommendations
    100 xp
    Improved non-personalized recommendations
    100 xp
    Combining popularity and reviews
    100 xp
    Non-personalized suggestions
    50 xp
    Finding all pairs of movies
    100 xp
    Counting up the pairs
    100 xp
    Making your first movie recommendations
    100 xp
  2. 3

    Collaborative Filtering

    Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.

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  3. 4

    Matrix Factorization and Validating Your Predictions

    Understand how the sparsity of real-world datasets can impact your recommendations. Leverage the power of matrix factorization to deal with this sparsity. Explore the value of latent features and use them to better understand your data. Finally, put the models you’ve discovered to the test by learning how to validate each of the approaches you’ve learned.

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Maggie MatsuiAmy Peterson
Robert O'Callaghan Headshot

Robert O'Callaghan

Director of Data Science, Ordergroove

Rob enables retailers and brands to make themselves indispensable to their customers’ lives by anticipating purchasing needs. Throughout his career, Rob has focused on the analysis, visualization, and modeling of data to produce actionable business improvements for some of the world’s largest organizations. He has successfully designed and implemented multi-million dollar machine learning solutions within several Fortune 500 companies, focusing in particular on bleeding edge unsupervised and supervised learning techniques. He has presented his work, in the U.S. and abroad, to audiences of hundreds at financial services and AI-focused conferences.
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