Grab the data. Train the model. Learn ML by doing.
Grab & Train is a hands-on machine-learning playground. Draw your own dataset, then train a real neural network right here in your browser and watch it learn the pattern - no code, no setup, no cloud. This is the whole idea of ML in one picture.
Every machine-learning project - from spam filters to image recognition - is really these three steps. You just did all three.
The dots you placed are labelled training data: each has features (its x, y position) and an answer (its colour). A model can only learn patterns that live in the data you give it - garbage in, garbage out.
The network starts with random weights and guesses badly. Each epoch it measures how wrong it is (the loss) and nudges every weight a little to do better - that's gradient descent. Watch the loss curve fall as it learns.
The coloured background is the model's decision boundary - its prediction for every point on the grid. High accuracy on your dots is good, but a boundary that wiggles around every single point has likely overfit and won't generalise.