Students are free to devise their own projects in Machine Learning for Kids, but I also write project worksheets to help inspire students and teachers.
This evening, I’ve written a new worksheet based around regression models. (I wrote about adding support for regression models to the site a couple months ago).
The premise for this project is sort of Space Invaders. Except with only a single Space Invader. And it’s a cute little bug.
The aim of the game is to shoot at the bug.
I’m on vacation for the Easter holidays this week. Apparently I don’t know how vacations work, so I’ve spent a lot of the last six days working on a major rewrite of a big chunk of Machine Learning for Kids. In this post, I want to describe what I’ve been doing and why.
In this post, I want to share a preview of a new feature I’m adding to Machine Learning for Kids, to ask for feedback and ideas for projects that it could be used to make.
I’ll start by contrasting this new feature with what I’ve done with Machine Learning for Kids before, then I’ll share screenshots of the new feature, and finally I’ve got a ten-minute video showing the sort of school lesson that I think it could be used for.
I added support for “local projects” (storing projects on your own computer) to Machine Learning for Kids this week. In this post, I want to give a little background.
Students can now create Machine Learning for Kids projects using EduBlocks – letting them create machine learning Python projects in the browser by dragging and dropping blocks on a canvas.
This is all thanks to a fantastic new contribution from Joshua Lowe.
Here’s a quick run-through to show what this makes possible.
One of the conference presentations I gave last year was a talk at Heapcon, sharing some stories of AI/ML lessons I’ve run in schools. The focus of the talk was how I’ve seen children understand and react to machine learning technologies.
I’ve since expanded the ideas in this talk into a mini-book at MachineLearningForKids.co.uk/stories but here is a recording of where some of these stories started.
In this post, I want to share a new Scratch extension that I made this week, explain what it does, and suggest a few ideas for the sorts of ways that it could be used.
The extension makes some of the data from the Spotify Audio Features API available as blocks in Scratch.
It means you can get numeric values representing different characteristics of songs, directly into a Scratch project.
In this post, I want to share a small tip from how I run Machine Learning for Kids: how I run instances of the site in different regions, and use geo-steering so that users are directed to the instance of the site nearest to them.
