Learning Path

Linear Algebra Fundamentals → Evaluation Protocols and Baselines

This advanced learning path equips AI and data engineers with the knowledge to design and evaluate recommender systems. It covers core techniques—collaborative filtering, content-based methods, matrix factorization, and neural recommenders—along with essential prerequisites in machine learning and linear algebra. The path culminates in practical evaluation strategies, ensuring learners can build and assess effective recommendation models.