Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.
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