Module 1 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
Machine learning is finding patterns in data instead of writing the rules yourself. That one shift is the whole thing. The loop never changes. Feed in data, let the model guess, measure how wrong it was, push it to be a little less wrong, run it again. That loop is training. Using the trained model on new data is inference.
A few words worth owning cold. Supervised, unsupervised, reinforcement. Features and labels. Parameters and weights. And the one that quietly kills projects: overfitting. That’s a model that memorized the training data and falls apart the second it sees something new. It’s why you hold back a test set the model never gets to study.
Why it matters
If you can’t explain this simply, you can’t sell it honestly. This is the vocabulary you need to talk to a technical buyer without bluffing. It’s also the ground every later call stands on, starting with the most useful one: is this even a machine learning problem, or a rules problem wearing a costume?
Build this
A 500-word piece called “ML for revenue leaders” that explains the loop with a sales analogy. If you can teach it in plain English, you understand it. If you can’t, you don’t. Publish it somewhere public.
How to work through it
- Watch the first 3Blue1Brown neural networks video for the intuition
- Work through the intro units of Google’s crash course
- Write the explainer and get it clean
- Write down the one thing that surprised you
Resources
- Google’s Machine Learning Crash Course
- StatQuest, for any concept that won’t click
- 3Blue1Brown’s Neural Networks series