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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.