Multi-Modal Learning Resource Recommendation Engine

Build a sophisticated recommendation system using collaborative filtering and content-based algorithms to suggest personalized learning resources. Utilize pandas for data processing, surprise library for recommendation algorithms, and create a Flask-based API that integrates student learning history, performance data, and resource metadata. Implement advanced filtering to recommend resources across multiple learning modalities (video, text, interactive).