Recommendation Systems in Machine Learning

Recommendation systems power modern digital platforms by delivering personalized item and content suggestions. Understanding how these algorithms operate is an essential capability for anyone working with data and machine learning. This text-based course provides a structured introduction to designing, implementing, and assessing recommendation engines. You will build a solid conceptual foundation, examine key algorithms, and study practical Python implementations for real-world personalization scenarios. What you'll learn: - Understand essential terminology, user-item interaction formats, and recommendation system architectures - Build collaborative filtering models using memory-based and matrix factorization techniques - Implement content-based filtering strategies using item metadata and feature representations - Explore modern vector search patterns and embedding representations for candidate generation - Address practical challenges such as the cold-start problem, data sparsity, and implicit feedback - Evaluate system performance with standard metrics including Precision@K, Recall@K, MAP, and NDCG The course begins with foundational concepts, terminology, and baseline heuristic methods before guiding you through structured algorithms and code implementations. Written exercises reinforce your understanding at each step. This course is suited for developers, data analysts, and aspiring machine learning engineers who want a clear, step-by-step introduction to recommendation algorithms. Start reading today to develop your own functional recommendation models.