Four-Project Series

Similarities and Recommender Systems you own this product

prerequisites
intermediate Python • basics of NumPy, pandas, scikit-learn • basics of machine learning
skills learned
read, process, and exploit user-item data • define and compute similarities between users and items using interactions • compute similarities between users and items in a recommender system
Alejandro Bellogin
4 weeks · 4-6 hours per week average · BEGINNER
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In this series of liveProjects, you’ll complete all the steps to build a recommendation system for a movie streaming website. Your system will need to learn the tastes of its users and predict which movies they might want to watch. You’ll work with movie data points like names, genres, and directors, and mapped interactions such as ratings and tags.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Collaborative Similarities

In this liveProject, you’ll build a movie recommendation system based on movies that your users have previously interacted with. This system is designed to boost engagement and keep your users on your site. You’ll develop a competitive array of similarity functions, and create your own recommender system based on these similarities. Finally, you’ll evaluate your system’s effectiveness and tune its parameters.

$29.99 FREE
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Project 2 Content-Based Similarities

In this liveProject, you’ll build a movie recommendation system based on the content and metadata of movies in your system. This system is intended to maximize the satisfaction of your movie-watching users. You’ll start with an analysis to determine the content of your movies, then use that data to implement content-based similarities for both products and users. You’ll build and evaluate your recommender system based on these connections, till it’s the best it can be!

$29.99 $19.99
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Project 3 Dimensionality Reduction

In this liveProject, you’ll build a movie recommendation system that reduces the dimensionality of the spaces involved to enhance its performance so that latent representations are identified, which aim to capture users and items interactions with the system on the same latent vector space. You’ll work to compute clusters on the movies and clients of your site, and utilize latent representations of users and items with a reduced dimensionality to compute similarities. Once you’ve established this data, you’ll build and test your recommendation engine.

$29.99 $19.99
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Project 4 Learning Similarities

In this liveProject, you’ll determine what similarities there are between certain movies, and then build a recommendation system based on the similarities you’ve identified. Although you’ll be working with movie data for a streaming website, identifying similarities can help enhance recommendation engines for any item or product. You’ll develop a general technique for spotting similarities, then apply your learned similarities to build a recommendation engine. You’ll then enrich your model with collaborative and content-based data, and evaluate and compare your model.

$29.99 $19.99
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project author

Alejandro Bellogin
Alejandro Bellogín is an Associate Professor at Universidad Autónoma de Madrid. Previously, he held a post-doctoral research grant with the Centrum Wiskunde and Informatica in Amsterdam, The Netherlands. He has supervised around ten Master Theses, one PhD thesis, and more than twenty Bachelor Theses on recommender systems. His publication history includes around 80 publications about different aspects of recommender systems.

Prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of data structures
  • Basics of NumPy
  • Basics of pandas
  • Basics of scikit-learn
  • Basics of Jupyter Notebook
TECHNIQUES
  • Algebra and calculus
  • Basics of machine learning
  • you will learn

    In this liveProject, you’ll learn how to build powerful recommendation systems using the most popular tools in the Python data ecosystem.


    • Read, process, and exploit user-item data
    • Define and compute similarities between users and items using interactions
    • Compute similarities between users and items in a recommender system
    • Define and compute similarities between users and items using content and metadata
    • Define recommender systems based on the definition of when two users or items are close
    • Compute similarities on top of learned latent representations based on user and item interactions
    • Define recommender systems based on similarity clusters and similarities from latent representations
    • Define recommender systems based on similarities
    • Analyze algorithm output
    • Tune model settings to improve its results

    features

    Self-paced
    You choose the schedule and decide how much time to invest as you build your project.
    Project roadmap
    Each project is divided into several achievable steps.
    Get Help
    While within the liveProject platform, get help from other participants and our expert mentors.
    Compare with others
    For each step, compare your deliverable to the solutions by the author and other participants.
    book resources
    Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
    includes 4 liveProjects
    liveProject $41.99 $59.99 self-paced learning
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