Intermediate · every module free · 9 challenges
The supervised setup, the cost function, and gradient descent doing the work.
Decision boundaries, the logistic link, and a cost function that comes from the likelihood.
What overfitting is, what regularization does about it, and how to choose without fooling yourself.
Neurons, layers, forward propagation and a training loop, before the deep learning track takes it apart.
The diagnostics that tell you what to fix, and the loop that turns them into decisions.
Purity, splits, forests and boosting -- the family that still wins on tabular data.
Clustering without labels, and finding the rare event you have almost no examples of.
Collaborative filtering, content-based retrieval and the two-stage system they become.
Returns, policies, the Bellman equation and the deep Q-network that approximates it.