Learning Path Recommendation System

Build a sophisticated recommendation system for personalized learning paths using collaborative filtering and content-based techniques. Utilize surprise library for matrix factorization, implement hybrid recommendation strategies combining student performance history, course metadata, and interaction patterns. Create a modular recommendation engine that can suggest courses, learning resources, and study strategies based on individual student profiles, previous performance, and learning style assessments.