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In this module, you will create three course recommendation systems using different methods. In lab 1, you will create a course recommendation system based on user profile and course genre matrices by computing an interest score for each course and recommend the courses with the highest interest scores. In the second lab, you will generate a course similarity matrix to create the recommendation system. In the third lab, you will implement a clustering-based recommender system algorithm using K-means clustering and principal component analysis based on group members’ course enrollment history. In labs four and five you will use collaborative filtering to make predictions about a user’s interest based on a collection of other users’ similar preferences. In lab 4, you will perform KNN-based collaborative filtering and in lab 5, you will use non-negative matrix factorization.