A guy who was the number one ranked machine learning competitor on Earth, twice, looked at how universities teach AI and decided they had the entire thing backwards.

So he built a free course that has turned more people into working AI practitioners than most graduate programs.

Jeremy Howard was the guy who made the course and it is called Practical Deep Learning for Coders.

Here is the argument that drives the whole thing.

Universities teach AI top-down. First you sit through linear algebra. Then calculus. Then probability. Then, maybe, a year later, you are finally allowed to touch a model. Howard watched this approach destroy motivated people. Most never made it to the part where it gets interesting. The math wall killed them first.

He thinks that is exactly wrong. His view is that you do not teach someone baseball by drilling the physics of a curveball for a year before letting them hold a bat. You let them play, then explain the physics once they care.

So his course inverts it. In the very first lesson, before any heavy theory, you train a working image classifier that actually runs. You build something real on day one. The theory comes later, pulled in piece by piece, exactly when you finally need it to go deeper.

Harvard Business Review said fast AI can take motivated students all the way to building industrial-grade AI systems.

The whole course is free. No paywall, no signup tricks.

It assumes you can code a little and remember some high school math. That's the bar.

The people who actually break into AI almost never start with the equations.

course.fast.ai

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