RecSys Fundamentals: Algorithms, Evaluation, and Deployment

Recommendation systems (RecSys) are the engine behind major platforms, driving content discovery and user engagement across e-commerce, media, and social networks. Mastering RecSys is essential for any modern data scientist working with large user bases. By the end of this course, you will possess the foundational knowledge and practical skills necessary to build and deploy personalized RecSys pipelines, from initial data processing to model serving and rigorous performance evaluation. What you'll learn: * Understand the core concepts of personalized recommendation, including collaborative filtering, content-based methods, and hybrid models. * Apply classical techniques like SVD and Alternating Least Squares (ALS) for efficient matrix factorization and implicit feedback modeling. * Configure modern deep learning architectures, such as Two-Tower models, for advanced candidate generation and retrieval efficiency using vector indexing. * Design multi-stage RecSys pipelines, covering candidate generation, scoring, ranking, and integrating real-time serving concepts. * Evaluate system performance using standard metrics (precision, recall) and advanced metrics focused on diversity and novelty. * Practice handling common challenges like data sparsity, the cold start problem, and bias in recommendation outputs. The course begins with foundational terminology and concepts, then progresses through hands-on practice with classical algorithms, culminating in the design principles for modern neural network-based recommendation architectures. This course is specifically designed for beginners in data science or machine learning who want a structured introduction to the field of RecSys, requiring no prior experience with recommendation algorithms. Start reading today and build your first intelligent recommendation engine.