Lecture
This lecture covers the implementation of recommender systems using the MovieLens dataset. The instructor demonstrates loading the dataset, exploring the number of users and movies, splitting the data, creating user-item matrices, and implementing item-based collaborative filtering. The lecture then progresses to writing code for similarity metrics, predicting ratings, and optimizing the solution. Finally, the instructor introduces user-based collaborative filtering, computes user similarities, and predicts ratings. The lecture concludes with an overview of evaluating recommenders using RMSE and MAE metrics in the Surprise library.