An Intelligent Hybrid AI Course Recommendation Framework Integrating BERT Embeddings and Random Forest Classification

With the proliferation of online learning platforms, selecting appropriate artificial intelligence (AI) courses has become increasingly complex for learners. This study proposes a novel hybrid AI course recommendation framework that integrates Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) for robust textual feature extraction, enhanced by a Random Forest classifier to improve recommendation precision. A curated dataset of 2,238 AI-related courses from Udemy was constructed through multi-session web scraping, followed by comprehensive data preprocessing. The system computes semantic and lexical similarity using cosine similarity and fuzzy matching to handle user input variations. Experimental results demonstrate a high recommendation accuracy=91.25%, precision=96.63%, and F1-score=90.77%. Compared to baseline models, the proposed framework significantly improves performance in cold-start scenarios and does not rely on historical user interactions. A Flask-based web application was developed for real-time deployment, offering instant, user-friendly recommendations. This work contributes a scalable and metadata-driven AI recommender architecture with practical deployment and promising generalization capabilities.