Build a Teachable Machine Image Classifier With Your Kid

Introduce your child to the fundamentals of machine learning by training a custom visual AI model in twenty minutes.

Most children understand that AI can generate text or images, but few understand how these systems actually learn. By building a custom image classifier, your child can transition from a passive consumer of AI to an active creator.

Teaching children aged 8 to 16 about artificial intelligence does not require complex coding environments or expensive subscriptions. Using Google's free, web-based tool, you and your child can build a fully functioning computer vision model. This hands-on project demonstrates how neural networks identify patterns in visual data, providing a foundational understanding of modern machine learning.

Step 1: Access the AI Lab

To begin, sit down with your child at a computer equipped with a standard webcam. Open a web browser and navigate directly to https://teachablemachine.withgoogle.com/ . Click on the "Get Started" button, then select "Image Project" followed by "Standard Image Model". This opens a clean, visual workspace divided into three main sections: Gathering Data, Training, and Previewing.

Step 2: Define Your Classes and Gather Training Data

An image classifier works by sorting visual inputs into distinct categories, which the platform calls "Classes". For this project, you will train the computer to recognize two different household objects or hand gestures (such as a thumbs-up versus a thumbs-down, or a coffee mug versus a pen).

First, rename "Class 1" to match your first object (for example, "Blue Pen"). Click the webcam icon within that class box. Instruct your child to hold the blue pen in front of the camera. Click and hold the "Record" button to capture at least one hundred images. Encourage your child to slowly rotate the pen, move it closer to the lens, and shift it to different areas of the screen. This variety helps the model learn what the pen looks like under different conditions.

Next, rename "Class 2" to match your second object (for example, "Red Apple"). Repeat the recording process, capturing another one hundred images of the second object from various angles and distances.