Build a Teachable Machine Image Classifier With Your Kid

Most children today are active consumers of artificial intelligence, whether they are interacting with voice assistants, playing games with AI-driven characters, or watching algorithmically curated videos. However, true digital literacy requires moving from consumption to creation. By building a functional machine learning model, children demystify the technology and realize that AI is simply a tool trained on data. Using a free, web-based tool, you and your child can build a custom image classifier in under thirty minutes. This hands-on project requires no coding experience, no paid subscriptions, and no specialized hardware, making it the perfect weekend activity to spark a lifetime of curiosity about computer science.

Demystifying Machine Learning with Teachable Machine

To build this project, we will use Google Teachable Machine, a web-based tool that allows anyone to train machine learning models quickly and easily. You can access the platform directly at https://teachablemachine.withgoogle.com/ . The platform uses a process called supervised learning, where we provide labeled examples of different objects, and the computer learns to recognize the patterns that distinguish them. For this activity, we will build a Rock, Paper, Scissors game helper that can instantly recognize which hand gesture your child is making in front of the webcam.

Step 1: Setting Up Your Classes

Once you open the Teachable Machine website, click on Get Started and select Image Project , then choose Standard Image Model . You will see a workspace with two default categories called Classes. Classes are simply the labels or categories that your model will learn to recognize. Since we are building a Rock, Paper, Scissors classifier, we need three classes:

• Rename Class 1 to Rock by clicking the pencil icon.
• Rename Class 2 to Paper .
• Click the Add a class button at the bottom and name Class 3 Scissors .

Step 2: Gathering the Training Data