A 25-session machine learning engineering course. Training pipelines, evaluation, and deployment — the engineering practice behind every working AI system.
Choose the right model class and set up data correctly — without bias, leakage, or inflated metrics.
Understanding gradient descent, loss functions, and the bias-variance tradeoff — not just the API calls.
Adapt an open-source model to a custom task and measure the improvement honestly.
Success metrics, failure modes, edge cases, and an automated test harness that catches problems before they ship.
Wrap, deploy, and call a live ML service on a real cloud platform — the full path from notebook to product.
Fewer than 3 in 100 applicants are selected.
Foundations and CS Specialist
"You can't build AI you don't understand. My job is to make the maths underneath it feel obvious - not intimidating."
Programming & Applied AI Specialist
"Students learn AI fastest when they're building something real with it. We start writing code on day one."
Programming & AI Engineering Specialist
"Real industry experience changes how you teach. I show students how AI is actually built, not just how it's described."
In the final four sessions, they build a complete end-to-end ML project — from raw data through a trained model to a deployed inference endpoint — defended live under Socratic questioning.
In the final four sessions, they build a complete end-to-end ML project — from raw data through a trained model to a deployed inference endpoint — defended live under Socratic questioning.
25 private 1-on-1 sessions with an IIT graduate mentor.
for the full 25-session course
$40 per 1-on-1 session · 25 sessions total
