The Ultimate Beginners Guide to Python Recommender Systems

Make movie recommendations using collaborative filtering! From beginning to end, implementations are described step by step in The Ultimate Beginners Guide to Python Recommender Systems course.

What you’ll learn

  • Understand the basics about recommender systems
  • Understand the theory and mathematical calculations of collaborative filtering
  • Implement user-based collaborative filtering and item-based collaborative filtering step by step in Python
  • Use the following libraries for recommender systems: LibRecommender and Surprise
  • Use the MovieLens dataset to generate movie recommendations for users



It is well known that recommender systems are hot topics in Artificial Intelligence and are widely used by a great many companies. Almost anywhere you look you can find recommendations for movies, music, videos, products, and services. A Netflix recommendation feature suggests you other films you might like when you finish watching a movie. This is the classic recommendation system example!

The use of recommender systems will be explained both in theory and in practice in The Ultimate Beginners Guide to Python Recommender Systems course! The algorithm will be based on the collaborative filtering technique used with movie recommendations (user-based filtering and item-based filtering). For testing all mathematical calculations, we are gonna use a small dataset. Secondly, we will test our analysis using the MovieLens dataset, which has more than 100.000 instances. Upon completion of The Ultimate Beginners Guide to Python Recommender Systems course (after implementing the algorithm from scratch), you will learn how to use two ready-made libraries: LibRecommender and Surprise!

Trending Now:   The Complete Python Course | Learn Python by Doing

The Ultimate Beginners Guide to Python Recommender Systems course is unique in that you will implement all mathematical calculations step by step from scratch in Python. As a first course on recommender systems, if you have never heard of them and don’t know how to implement them, you will have all the theoretical and practical knowledge to develop some simple projects and to take more advanced courses. I look forward to seeing you in class!

Who this course is for:

  • People interested in recommender systems
  • Students who are studying subjects related to Artificial Intelligence
  • Data Scientists who want to increase their knowledge in recommender systems
  • Professionals interested in developing recommender systems
  • Beginners who are starting to learn recommender systems

Created by Jones Granatyr, IA Expert Academy
Last updated 6/2021
Size: 1.1 GB

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