Collaborative FilteringRecommender SystemsEvaluationLensKitMatrix FactorizationSummary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft Excel
Recommender Systems Specialization
Master recommender systems.. Learn to design, build, and evaluate recommender systems for commerce and content.
Offered By
Recommender Systems SpecializationUniversity of MinnesotaWhat you will learn
Build recommendation systems
Implement collaborative filtering
Master spreadsheet based tools
Use project-association recommenders
Skills you will gain
About this Specialization
7,940 recent views
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.
Learner Career Outcomes
60%
Started a new career after completing this specialization.
11%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Some related experience required.
Approx. 5 months to complete
Suggested 3 hours/week
English
Subtitles: English, Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, Spanish
Learner Career Outcomes
60%
Started a new career after completing this specialization.
11%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Some related experience required.
Approx. 5 months to complete
Suggested 3 hours/week
English
Subtitles: English, Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, Spanish
There are 5 Courses in this Specialization
Introduction to Recommender Systems: Non-Personalized and Content-Based
562 ratings
This 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 summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Nearest Neighbor Collaborative Filtering
282 ratings
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.
Recommender Systems: Evaluation and Metrics
210 ratings
In 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, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses.
Matrix Factorization and Advanced Techniques
168 ratings
In 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 recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Offered by
University of Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
How long does it take to complete the Specialization?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
What will I be able to do upon completing the Specialization?
What is the honors track?
How does this Specialization relate to the prior Recommender Systems courses?
More questions? Visit the Learner Help Center.
