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!
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
- Programming logic
- Basic Python programming
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