activity summary recommender systems what are recommender systemsuser-based collaborative filtering limitations of user-based collaborative filtering item-based collaborative filtering understanding item-based collaborative filtering how item-based collaborative filtering workscollaborative filtering using python finding movie similarities understanding the code the corrwith function improving the results of movie similarities making movie recommendations to people understanding movie recommendations with an example using the groupby command to combine rows removing entries with the drop command improving the recommendation results summary more data mining and machine learning techniques -nearest neighbors concepts using knn to predict rating for movie activity dimensionality reduction and principal component analysis dimensionality reduction principal component analysis pca example with the iris dataset activity data warehousing overview etl versus elt reinforcement learning -learning the exploration problem the simple approach the better way fancy words markov decision process dynamic programming [