University of Minnesota, Twin Cities - Recommender Systems Specialization
- Offered byCoursera
- Public/Government Institute
Recommender Systems Specialization at Coursera Overview
Recommender Systems Specialization
at Coursera
Duration | 5 months |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Recommender Systems Specialization
Table of content
Recommender Systems Specialization at Coursera Highlights
Recommender Systems Specialization
at Coursera
- Earn a Certificate upon completion from university of Minnesota
Financial aid available
Recommender Systems Specialization at Coursera Course details
Recommender Systems Specialization
at Coursera
Skills you will learn
More about this course
- 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
Recommender Systems Specialization at Coursera Curriculum
Recommender Systems Specialization
at Coursera
Introduction to Recommender Systems: Non-Personalized and Content-Based
Nearest Neighbor Collaborative Filtering
Recommender Systems: Evaluation and Metrics
Matrix Factorization and Advanced Techniques
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