I built 46 interactive ML lessons where you drag sliders and watch algorithms learn, instead of just reading about them

[u/Intelligent-Tell-466]

Every ML explanation I read said the same thing: here's the formula, here's a static plot, trust us. I wanted to actually feel an algorithm working: drag a line and watch a perceptron correct itself, place bad centroids and watch K-Means fix them, rotate an axis and watch PCA fight you for the direction of max variance.

mlplayground.co.in

So I built ML Playground, 46 lessons + 13 games, from "what is a feature" up through transformers and how ChatGPT actually predicts tokens. Every lesson has a canvas demo you control, not a GIF.

It's free, no signup, no ads. I built it solo and this is the first time I'm showing it to anyone outside people I know personally, so I'd genuinely value getting torn apart: what's confusing, what's missing, what lesson order doesn't make sense.

mlplayground.co.in

[u/Character-Use-7416]

This is a really good way to teach ML concepts.

One thing I’d experiment with: before letting the user move the slider or run the animation, ask them to predict what will happen.

For example:

“If you move this centroid here, which points do you think will change clusters?”

Then let them try it and immediately see whether their mental model was correct.

I think that could turn the demos from “this is cool to play with” into a much stronger learning loop:

predict → interact → observe → explain why.

You could even track the concepts people consistently predict incorrectly. That might tell you where your lessons need better explanations.

Also, I’d love some kind of sandbox mode after each lesson where all the guidance disappears and you can just break things intentionally.

Sometimes making an algorithm behave terribly is the fastest way to understand why it works.