If I was starting from zero today, here's exactly how I'd approach learning AI and machine learning:

Step 1: Python fundamentals (1-2 weeks)

Don't jump into TensorFlow day one. Get comfortable with Python — loops, functions, numpy, pandas. That's your foundation.

Step 2: Understand the math intuitively (not formally)

You don't need a math degree. Learn linear algebra and calculus visually — 3Blue1Brown's series is unmatched for this. Understand what a gradient is, not just the formula.

Step 3: Build your first model from scratch (week 3)

A simple linear regression in pure Python. No libraries. This teaches you what's actually happening under the hood when you call model.fit().

Step 4: Move to real frameworks

Now learn PyTorch or TensorFlow. Build a digit classifier (MNIST). Then a sentiment analyzer. Each project should take 2-3 days.

Step 5: Pick a specialization

Computer vision? NLP? Reinforcement learning? Generative AI? Pick ONE and go deep. Specialization is where the money is.

The biggest mistake beginners make: consuming tutorials without building. Every hour of learning should be matched with an hour of coding.

I'm building SimuFed to be the place where beginners go from "what is AI?" to actually deploying models. It's free — come learn with us.