Recommender Systems Specialization
Master Recommender Systems. Learn to design, build, and evaluate recommender systems for commerce and content.
About This Specialization
Follow the suggested order or choose your own.
Designed to help you practice and apply the skills you learn.
Highlight your new skills on your resume or LinkedIn.
- Intermediate Specialization.
- Some related experience required.
Introduction to Recommender Systems: Non-Personalized and Content-BasedUpcoming session: Nov 20
- 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
About the CourseThis course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using
Nearest Neighbor Collaborative FilteringUpcoming session: Nov 27
About the CourseIn 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 simila
Recommender Systems: Evaluation and MetricsUpcoming session: Nov 20
About the CourseIn this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity,
Matrix Factorization and Advanced TechniquesUpcoming session: Nov 27
About the CourseIn this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building rec
Recommender Systems CapstoneUpcoming session: Jan 22
- 1-3 weeks of study, 3-5 hours per week
About the Capstone ProjectThis capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a ca
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand