Aligned with Machine Learning (Sem 6)

Machine Learning

Go beyond Jupyter notebooks. Learn to build, deploy, and monitor real ML models — the way it's done in production at actual companies.

Weeks

Basic

Coding Helpful

Real Project

100%

Hands-On

What You'll Learn — Week by Week

College teaches ML in Jupyter notebooks. Industry runs ML in production. This course bridges that gap and makes you hireable as an ML engineer.

Week 1 — Organizing ML Projects ProperlyMove beyond messy notebooks. Learn how professional ML teams structure projects, track experiments, and version their data and models. Lab: Set up a well-structured ML project with experiment tracking
Week 2 — Turning Models into APIsTake a trained ML model and turn it into a web API that other applications can call. Learn FastAPI and Docker for ML. Lab: Containerize an ML model and serve it as a REST API
Week 3 — Deploying ML on the CloudPut your ML model into production on AWS or Azure so it can handle real traffic with auto-scaling. Lab: Deploy your ML model to the cloud with auto-scaling endpoint
Week 4 — Monitoring & Improving ModelsSet up monitoring to track model performance in production. Detect when models get stale and need retraining. Lab: Build a monitoring dashboard that tracks your model's performance
Week 5 — ML Pipeline & Final ProjectSet up automated pipelines that train, test, and deploy ML models automatically. Bring everything together. Final Project: Complete production ML system with CI/CD, deployment, monitoring, and auto-retraining

You start building immediately. By week 2, you'll have a working ML API that you can access from anywhere.

Knowing ML theory isn't enough. Companies need people who can deploy, monitor, and maintain ML systems in production.

Maps to Machine Learning (Sem 6) — takes what you learn in theory and shows you how to actually use it in the real world.

Learn MLflow, FastAPI, Docker, cloud ML services — the exact tools used by ML engineers at companies like Uber, Netflix, and Amazon.