Recommendation Systems

About this course

Implement collaborative filtering (matrix factorization, neighborhood methods) and content-based recommendation. Design hybrid systems, handle cold start, and evaluate with precision, recall, and diversity metrics.

• 20 lessons
• 2 h
• advanced

What you'll learn

• Define recommendation systems and their role in information retrieval
• Identify major types of recommendation approaches
• Explain the principle of collaborative filtering
• Differentiate between user-based and item-based approaches
• Implement user-based and item-based neighborhood methods
• Evaluate the strengths and weaknesses of neighborhood approaches

Finishing the course

• 80 knowledge-check questions along the way, so you know the ideas stuck before you move on.
• Watching is free and needs no account. Sign in to save your progress. Pass each lesson's quiz (60%+) to earn the certificate. Each certificate has its own public verification link to share, marked quiz-verified.

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