These notes represent my own study jottings and learning synthesis, not an authoritative or foolproof standard. If you find them helpful, feel free to use them. If you spot anything that could be clearer or have suggestions for additional concepts to include, issues and pull requests are warmly welcome.
Foundations
Core building blocks and fundamental data terminology
01. Foundations
The vocabulary and structural building blocks of machine learning.
Most introductions to machine learning begin with algorithms. This chapter begins one level lower, with the objects every algorithm consumes: datasets, rows, columns, labels, and the split between data used for learning and data used for judging what was learned.
These ideas look simple, and in a narrow sense they are. But most production failures start here: a label that measured the wrong thing, a test row that also appeared in training, a feature that quietly contained the answer. If you understand this chapter well, the later math has something concrete to attach to.