Recommender Systems with Machine Learning - Item-Based Collaborative Filtering

The video tutorial covers item-based collaborative filtering, starting with an introduction to collaborative filtering types. It details the steps for data preparation, including merging datasets and using libraries like pandas and numpy. The tutorial explains how to gain data insights using matplotlib and implement K Nearest Neighbors (KNN) for item-based filtering. It guides viewers through building a recommendation engine, testing it, and using random sampling for book recommendations. The session concludes with a summary and instructions to start with Jupyter Notebook.

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