Recommender Systems

University of Minnesota via Coursera Specialization

Coursera Specialization based on 707 ratings

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Overview

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,...

Syllabus

Course 1: Introduction to Recommender Systems: Non-Personalized and Content-Based- Offered by University of Minnesota. This course, which is designed to serve as the first course in the Recommender Systems specialization, ... Enroll for free.Course 2: Nearest Neighbor Collaborative Filtering- Offered by University of Minnesota. In this course, you will learn the fundamental techniques for making personalized recommendations ... Enroll for free.Course 3: Recommender Systems: Evaluation and Metrics- Offered by University of Minnesota. In this course you will learn how to evaluate recommender systems. You will gain familiarity with ... Enroll for free.Course 4: Matrix Factorization and Advanced Techniques- Offered by University of Minnesota. In this course you will learn a variety of matrix factorization and hybrid machine learning techniques ... Enroll for free.Course 5: Recommender Systems Capstone- Offered by University of Minnesota. This capstone project course for the Recommender Systems Specialization brings together everything ... Enroll for free.