A new and updated introduction to computer science as a tool to solve real-world analytical problems using Python 3.5
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.
You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Course 2 of 5 in the Recommender Systems Specialization.
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
User-User Collaborative Filtering Recommenders Part 1
User-User Collaborative Filtering Recommenders Part 2
Graded: User-User CF Answer Sheet
Graded: User-User Collaborative Filtering Quiz
Graded: User-User CF Programming Assignment
Item-Item Collaborative Filtering Recommenders Part 1
Item-Item Collaborative Filtering Recommenders Part 2
Graded: Item Based Assignment Part l
Graded: Item Based Assignment Part II
Graded: Item Based Assignment Part III
Graded: Item Based Assignment Part IV
Graded: Item-Item CF Programming Assignment
Advanced Collaborative Filtering Topics
Graded: Item-Based and Advanced Collaborative Filtering Topics Quiz