Designing Netflix-Style Recommendation Systems: Problem Formulation

How do streaming platforms keep millions of users engaged hour after hour? The secret lies in translating vague business goals into precise, data-driven machine learning problem statements. In this course, you will learn how to design a recommendation system inspired by top streaming platforms, starting with core problem formulation. You will discover how to translate user behaviors into actionable implicit feedback data, define clear optimization metrics, and structure a scalable recommendation pipeline. What you'll learn: 1. Understand foundational recommendation system concepts, including collaborative filtering. 2. Formulate business goals into structured machine learning problem statements. 3. Analyze implicit feedback signals like watch time and clicks to model user preferences. 4. Evaluate recommendation quality using modern metrics such as NDCG. 5. Incorporate modern vector embeddings and similarity search concepts into your system design. This text-only course guides you step-by-step from basic recommendation terminology to system formulation concepts through clear explanations and written exercises. This course is designed for beginners, aspiring data scientists, and product managers with no prior machine learning experience required. Start learning today and master the art of designing intelligent recommendation systems.