TechnologyBeginner

Recommender Systems: Principles, Methods, and Applications

Name: Recommender Systems: Principles, Methods, and Applications
Price: 499 INR
Availability: InStock

This course provides a comprehensive introduction to recommender systems, covering collaborative filtering, content-based recommendation, knowledge-based approaches, and hybrid methods. Students will learn to design, evaluate, and explain recommendation algorithms, and explore advanced topics like matrix factorization and case-based reasoning. The course includes practical examples and case studies to illustrate real-world applications.

What You'll Learn

Explain the fundamental concepts and purposes of recommender systems

Implement collaborative filtering techniques, including user-based and item-based nearest neighbor methods

Build content-based recommendation systems using vector space models and TF-IDF

Design knowledge-based recommenders using constraint satisfaction and case-based reasoning

Evaluate recommender systems using appropriate metrics and methodologies

Apply hybrid approaches to overcome limitations of individual recommendation techniques

About this course

Recommender Systems: Principles, Methods, and Applications is a self-paced beginner-level technology online course on GreyAcademy, taught by Anurag Trivedi. It has 3 modules and 14 chapters, and it takes about 10 hours to complete. Enrol once and learn at your own pace. You get a completion certificate when you finish all the lessons.

Topics covered: Preface, Introduction to basic concepts, Data sparsity and the cold-start problem, Content-based recommendation, Explicit decision models, PROD, end procedure, Offline, Feature augmentation hybrids, Explanations in constraint-based recommenders, Introduction, Analysis of results and more.

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