Follow them in order for the full journey, or jump into any path whose prerequisites you meet.
Path 1
Build the mathematical foundation every AI practitioner needs — algebra, linear algebra, calculus, probability and optimization — explained from absolute zero.
Path 2
Learn how machines learn from data: core algorithms, honest evaluation, and the practical workflow used in industry — no prior experience required.
Path 3
Neural networks from a single neuron to transformers: how modern AI systems see, read and generate — built up step by step.
Path 4
The big picture: from search and reasoning to LLM agents, ethics and where the field is heading — the path that ties everything together.