Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 54 Minute(s)
Language
English
Taught by
Jones Granatyr, AI Expert Academy
Rating
1 views
Course Overview
Use collaborative filtering to recommend movies to users! Implementations step by step from scratch!
Recommender systems are a hot topic in Artificial Intelligence and are widely used for a lot of companies. They are everywhere recommending movies, music, videos, products, services, and so on. For example, when you finish watching a movie on Netflix, other movies you might like are indicated for you. This is the classic example of a recommender system!
In this course, you will learn in theory and practice how recommender systems work! You will implement an algorithm based on the collaborative filtering technique applied to movie recommendations (user-based filtering and item-based filtering). We are going to use a small dataset to test all mathematical calculations. Then, we will test our algorithm using the famous MovieLens dataset, which has more than 100.000 instances. At the end of the course (after implementing the algorithm from scratch), you will learn how to use two pre-built libraries: LibRecommender and Surprise!
What makes this course unique is that you will implement step by step from scratch in Python, learning all mathematical calculations. This can be considered the first course on recommender systems, so, if you have never heard about how to implement them, at the end you will have all the theoretical and practical background to develop some simple projects and also take more advanced courses. See you in class!
Course Content
- 6 section(s)
- 30 lecture(s)
- Section 1 Introduction
- Section 2 Search for similar users
- Section 3 Collaborative filtering - user-based filtering
- Section 4 Collaborative filtering - item-based filtering
- Section 5 Libraries for recommender systems
- Section 6 Final remarks
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
Reviews
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GGaro Sarajian
Good for beginners, doesn't really cover much of the math so I'm not sure how much it'll stick.
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MMauro Yoel Enriquez
Es bueno solo para saber los conceptos basicos para un caso generico, me hubiera gustado saber mas sobre el uso de la libreria Surprice pero bueno.
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FFomba Kassoh
She is very thorough and the best explainer I have come across in Udemy.
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RRishabh Gaur
It was a good start for Recommendation systems.
