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InicioPythonBuilding Recommendation Engines in Python

Building Recommendation Engines in Python

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

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4 Horas16 Videos60 Ejercicios
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Descripción del curso

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.
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  1. 1

    Introduction to Recommendation Engines

    Gratuito

    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.

    Reproducir Capítulo Ahora
    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.

    Reproducir Capítulo Ahora
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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

Sets De Datos

User ratingsMovies

Colaboradores

Collaborator's avatar
Amy Peterson
Collaborator's avatar
Maggie Matsui
Robert O'Callaghan HeadshotRobert O'Callaghan

Director of Data Science, Ordergroove

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