A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
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A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
What's inside
Five courses
Introduction to Recommender Systems: Non-Personalized and Content-Based
(0 hours)
Introduction to Recommender Systems: Non-Personalized and Content-Based
(0 hours)
Nearest Neighbor Collaborative Filtering
Nearest Neighbor Collaborative Filtering
Recommender Systems: Evaluation and Metrics
Recommender Systems: Evaluation and Metrics
Matrix Factorization and Advanced Techniques
Matrix Factorization and Advanced Techniques
Recommender Systems Capstone
(0 hours)
Recommender Systems Capstone
(0 hours)Learning objectives
- Build recommendation systems
- Implement collaborative filtering
- Master spreadsheet based tools
- Use project-association recommenders
- Build recommendation systems
- Implement collaborative filtering
- Master spreadsheet based tools
- Use project-association recommenders
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