ML Concepts and Landscape covers: The Hundred-Page Machine Learning Book, Alpaydın's Machine Learning, AIMA. Build a mental map of the entire ML field before going hands-on. Understand what ML is, what the major approaches are, what problems it solves, and where the field is heading. This is the integrator's orientation — see the whole forest before planting trees.
This course unlocks once you've finished its prerequisite. Open prerequisite →
This is the map-drawing module. You're not building anything yet — you're standing on a hill and looking at the whole territory before you walk into it.
There is a specific kind of lost you feel when a field is new: every article assumes you know six terms you've never met, and every one of those terms unpacks into six more. You k…
Here's a question that sounds philosophical and is actually an engineering question: when you shipped that pricing logic — the one with the thresholds you tuned by hand after thre…
Every field has one book that sits on the shelf like a monument — the one that's on every syllabus, in every bibliography, and almost nobody has read end to end. For AI it's Russe…
Work through each item before the checkpoint.
Name and describe every major ML algorithm family. Explain supervised vs unsupervised vs reinforcement learning with examples.
6 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.