Machine Learning Fundamentals
Most ML courses bury you in math before you touch a model. Learn linear models, trees, ensembles, and evaluation in a way that actually sticks, with the math kept in service of the ideas.
Deep Learning From Scratch
You can use a PyTorch model and you have no idea what it is doing. Build backprop, CNNs, RNNs, and transformers up from scratch, so the architecture stops being a black box.
MLOps & Model Deployment
You trained a model and now nobody knows what to do with it. Learn the serving, monitoring, and retraining work that takes ML from notebook to production.
Feature Engineering & Data Prep
Models get the credit but features do the work. Learn the data prep, leakage checks, and reproducibility patterns that decide whether your model is any good.
Reinforcement Learning Applied
RL went from academic to mandatory the day RLHF made GPT useful. Learn the fundamentals plus RLHF and RLAIF, so you understand the alignment work everyone is talking about.