AI Learning Path Optimizer
Personalized learning recommendations powered by machine learning.
AI Learning Path Optimizer is a full-stack AI/ML application designed to help learners identify what to study next and organize their learning journey. It combines a trained recommendation model with a FastAPI backend, web interface, and persistent database layer to deliver structured learning-path recommendations.
Technology stack
Problem
Learners often struggle to identify the right skills, topics, and learning sequence for their career goals. This project aims to make learning recommendations more structured and accessible through an intelligent recommendation system.
Approach
Developed a machine-learning recommendation service and integrated it with a FastAPI backend. Built API endpoints, a browser-based interface, database models, and a model-serving workflow. Prepared the application for containerized production deployment.
Solution
Built a full-stack learning-path recommendation platform with trained model artifacts, backend APIs, a web interface, and database persistence. Added Docker-based deployment configuration and production runtime support.
Results
Implemented the recommendation pipeline and integrated the model into a deployable web application. Added PostgreSQL migration support, application readiness checks, and production deployment configuration. Model performance metrics should be added after confirming the final evaluation results.