Build, fine-tune, and ship AI systems. Not toy notebooks — real applications running in production, evaluated rigorously, and improved over time.
Every module pairs theory with hands-on application. By the end, you'll have shipped real work in each one.
Linear regression to gradient boosting. The classical methods that still solve 80% of problems.
PyTorch, neural network design, training dynamics. Build from scratch before reaching for libraries.
How modern language models work, where they break, and how to build robust applications on top.
LoRA, QLoRA, instruction tuning. Customize open-source models for real use cases.
How to know your model is actually good — and how to keep it that way in production.
Ship a real AI feature with a partner team — running alongside the curriculum. Own it from prototype to production.
From data prep through training to evaluation. No copy-paste — understand every step.
MLOps basics, model monitoring, handling drift. The unglamorous stuff that keeps systems running.
Build evals that actually measure what you care about. Spot when benchmarks are lying.
AI/ML hiring is competitive. Real shipped work and rigorous evaluations are how you stand out.
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