This Specialization equips learners with practical skills to design and implement robust recommendation systems using Python. Spanning foundational techniques to hybrid models, it covers collaborative filtering, content-based filtering, and real-world deployment strategies using libraries like Surprise, Pandas, and Scikit-learn. Learners will explore use cases like movie and book recommenders, applying best practices from real-world platforms.
Applied Learning Project
Learners will complete hands-on projects including building book and movie recommendation engines. Through step-by-step coding exercises, they’ll develop systems that compute personalized recommendations, apply text similarity models, and evaluate predictive performance using real datasets.
