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Step-By-Step Guide to Build Collaborative Filtering and Association Rule Based Recommender Using Fast.AI and Python
Updated on Sep, 2026
Programming, Data Science, Machine Learning
Training 5 or more people ?
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Recommender system is a promising approach to boost sales to the next level by suggesting the right products to the right customers.
This course starts by showing you the main solutions of recommender systems in the industry and the hypotheses behind the main solutions. You’ll then learn how to build collaborative filtering models with fast.ai, and exercise the trained model on test datasets.
As you advance, you’ll visualize latent features, interpret weights and biases, and check what similar users/Items are from the model’s perspective. Furthermore, you’ll build a hybrid recommender system with popularity and association rule, and evaluate the recommendations with selected criteria.
By the end of this course, you’ll be able to explain the theories and assumptions of recommender systems and build your own recommender on other datasets using python.
The outline of course is as follows:
Why Business Needs Recommender Systems
Roadmap of the Course
The Hypotheses Behind the Main Solutions of Recommender Systems
Hands-on Collaborative Filtering Recommender System With Fast.ai on Instacart Grocery Dataset
A Quick Eda on the Grocery Dataset
What Is Collaborative Filtering in Depth
How to Build and Train Collaborative Filtering Model With Fast.ai
How to Visualize Latent Features? Do Popular Items Have a Higher Bias? What Are Similar Users From Model Perspective?
Step-By-Step Guide to Build a Hybrid Recommender System With Popularity and Association Rule
What Is the Definition of Popularity and What Is Support
How to Encode an Item-Order Matrix
What Are Confidence and Lift
What Is Association Rule and How to Apply Apriori Algorithm
How to Evaluate Results With Selected Criteria
End-Of-Course Conclusion
Understand the hypotheses behind the main solutions of recommender systems
Build and train collaborative filtering models with fast.ai
Exercise the trained model on test datasets
Fetch and visualize latent features
Compare and interpret weights and biases
Compute support, confidence, and lift
Encode an item-order matrix
Apply association rule and apriori algorithm
Evaluate results with selected criteria
Check out the detailed breakdown of what’s inside the course
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