Build a Teachable Machine Image Classifier With Your Kid

Issue #32 • March 13, 2024

Most children experience artificial intelligence as a passive consumer, whether they are watching recommended videos or asking a chatbot to write a story. To truly prepare the next generation for an AI-driven world, we must shift their perspective from consumers to creators. By building a custom image classifier, kids aged eight to sixteen can demystify how computers learn to see. Using a free, web-based tool and a standard webcam, you can help your child train their very first machine learning model in less than thirty minutes.

Demystifying Computer Vision

When a self-driving car stops for a pedestrian or a smartphone unlocks via facial recognition, they are using computer vision. To a child, this can feel like magic. In reality, it is simply a mathematical model trained on thousands of images. To teach this concept, we will use Google's Teachable Machine, a free, highly visual platform that requires no coding, no paid subscriptions, and no specialized hardware.

In this project, you and your child will build a Rock-Paper-Scissors classifier. The goal is to train the computer to recognize these three distinct hand gestures through the webcam and accurately predict which gesture is being shown in real time. This hands-on exercise provides a clear, tangible demonstration of how machine learning models are built, trained, and tested.

Step-by-Step Project Guide

Follow these steps with your child to build, train, and test your custom image classifier:

Step 1: Set Up Your Classes

Open a web browser on any computer with a webcam and navigate to Teachable Machine . Click on "Get Started" and select "Image Project", then choose "Standard Image Model". You will see a interface with two default classes: Class 1 and Class 2. Rename Class 1 to "Rock" and Class 2 to "Paper". Click the "Add a class" button to create a third class and name it "Scissors".

Step 2: Gather Your Training Data