6 lessons
NumPy and pandas, the two libraries under everything
The real tools used to do vector math and load real datasets, instead of the plain-Python loops used throughout this course.
Visualizing data and models with Matplotlib
Turning every diagram drawn in text throughout this course into real plots you generate from your own data and models.
scikit-learn, every classical model in a few lines
How linear regression, logistic regression, trees, forests, SVMs, k-means, and PCA look in the library actually used to build them in practice.
PyTorch, from your hand-written network to a real framework
Mapping every piece of the perceptron and backpropagation lessons directly onto PyTorch's tensors, autograd, and training loop.
An end-to-end project walkthrough
Every lesson in this course, applied in order, to one real dataset from raw CSV to a trained, honestly-evaluated model.
Cheat sheet: NumPy, pandas, Matplotlib, scikit-learn, PyTorch
A single quick-reference page for every tool covered in this module - the commands you'll actually reach for, without the explanations.