The biggest myth in tech right now: you need years of math and a computer science degree to work with AI.

I've seen complete beginners go from "what is Python?" to deploying their own ML models in under 8 weeks. Here's the actual path:

Week 1-2: Python fundamentals. Not all of Python — just the 20% you'll use 80% of the time. Lists, functions, loops, and libraries like NumPy and Pandas.

Week 3-4: Data thinking. Before you touch any ML algorithm, you need to understand how to clean, explore, and visualize data. This is the unglamorous work that makes everything else possible.

Week 5-6: Your first models. Linear regression, decision trees, random forests. These aren't fancy, but they solve real problems and teach you how ML actually works under the hood.

Week 7-8: Neural networks & deployment. This is where it gets exciting. Build a simple neural net, train it on real data, and push it live so anyone can use it.

The tools have never been more accessible. The community has never been more supportive. The only thing standing between you and building AI is starting.

That's exactly what we're doing inside ML Mastery — structured courses for complete beginners, a community of builders, and hands-on projects you can actually show off.