Ten hours that cover the whole territory: how these systems work, what they physically run on, how to get real output from them, what the law now requires, how to buy and roll them out, and how to turn all of it into three habits your team can see by next month.
Later sections assume the vocabulary of earlier ones. Modules C and D are readable out of order by a compliance or management reader who skips the hands-on middle.
Sections 02 and 03 are the depth pair: how a model works inside, and what it physically runs on. Both are skippable if you are short of time.
Nobody has ten hours in their first week. Pick a path and the course hides what you do not need. You can always come back for the rest.
The large platforms teach AI in pieces: a prompting short course here, a specialization on machine-learning theory there, a governance module sold separately to enterprise. This is one program that covers the mechanics, the hardware, the daily practice, the law, the buying decision and the rollout, with a checkpoint at the end of every section.
Three kinds of content age at different speeds, so they are authored and reviewed separately. Pages carrying figures show "current as of"; legal pages carry a named reviewer.
Eleven years building and governing machine-learning systems inside regulated industries: insurance pricing, clinical triage, and financial crime. She has sat on both sides of the table: shipping the models, and later signing off on other people's.
This course is the briefing she used to give every new director in their first week: the one that made the difference between people who could ask a useful question and people who could only ask for a demo.
One payment, lifetime access, checkpoints that mean something, and a 30-day plan you actually leave with.