A new reference rounds up the core ML equations—Bayes’ Theorem, cross-entropy, eigen decomposition, attention—and shows how they plug into real Python code using NumPy, TensorFlow, and scikit-learn.
It hits the big four: probability, linear algebra, optimization, and generative modeling. Stuff that fuels classifiers, neural nets, PCA, transformers—aka everything current ML leans on.
Why it matters: As models get fancier, having your math chops in order isn’t optional.
