The path from tensor operations to production ML systems.

Set this up right once. Save hundreds of hours forever.

Not textbook math. The specific math that explains WHY every ML algorithm works.

Python deeper than 90% of ML engineers. SQL as fluently as English.

ML is 80% data. The engineer who can build reliable data pipelines is the most valuable on the team.

Go deep here. 80% of production ML runs on these algorithms, not deep learning.

The skill that wins Kaggle competitions and gets models into production. Often more impactful than algorithm choice.

Beyond the basics — the algorithms that power production ML at scale.

Bad evaluation = wrong decisions. This is where most engineers make expensive mistakes.

When to use deep learning, how to use it effectively, and when classical ML is better.

From pixels to predictions — CNNs, vision transformers, detection, and multimodal vision-language models.

Text data is everywhere. Learn to work with it directly before you lean on a pretrained transformer for everything.

The architecture that ate ML. Attention, pretraining, MoE, fine-tuning, alignment, RAG, agents, and serving.

From manual tuning to intelligent search. This separates good ML engineers from great ones.

The gap between a Jupyter notebook and a production ML system. Most companies struggle here.

Getting your model from a file on disk to serving real predictions at scale.

Models degrade silently in production. Monitoring is what catches it before customers do.

When your data doesn't fit in RAM. When training takes days. When you need to scale.

Pick one or two to go deep. These are where most ML engineering jobs actually live.

Architect production ML systems that scale. This is what senior ML engineers get paid for.

Senior ML engineers are expected to think beyond accuracy. Ethics is an engineering problem.