WHAT WE ARE MAKING

WHY WE ARE MAKING IT

You will choose a topic that interests you, gather real examples from the world around you, and use them to train an AI model that can recognize and classify what it sees.

To understand how machine learning by building it yourself, from collecting data to watching your model make predictions.

Final Project Requirements

A 2-minute share-out: your topic, your results, and one thing your model surprised you with.

Screenshots of your model tested on 5 new images per category, with notes on what it got right and wrong.

A trained Teachable Machine model with at least 3 categories and 10+ images per category.

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You will go on a scavenger hunt but instead of collecting objects, you'll collect data. Pick any topic that interests you, search real-world images across at least three categories, and use them to train an AI model that can recognize and classify what it sees. Along the way, you'll discover something every machine learning engineer knows: the data you collect is just as important as the model you build.

Activity Prompt

The Data Scavenger Hunt is part of that supervised learning workflow because the AI is being given labeled examples.

Supervised Learning

؜2nd

3rd

4th

Choose Your Topic

Scavenger Hunt

Train Your Model

Test & Reflect

ACTIVITY STEPS

1st

Pens/Markers

Pencils

Paper

Laptop

Phone

ACTIVITY MATERIALS

A classification problem is when you want an AI to sort things into distinct groups.

Instead of giving the AI rules, you show it lots of examples from each group and it figures out the patterns on its own.

Most AI you interact with every day solves classification problems like photo apps that recognize faces

CLASSIFICATION PROBLEM

Teachable Machine is a free tool by Google that lets you train a machine learning model directly in your browser without coding. You upload your examples, click Train, and it builds a neural network that can classify new images in real time.

You can test your model live using your webcam or by uploading new images, and see exactly how confident the model is about each prediction.

TEACHABLE MACHINE

1st

Collect 3 Object you have with you

2nd

Go to teachablemachine.withgoogle.com

1. Select "Image Project" then "Standard image model"
2. Create one class for each of object and give each class a name
3. Hold on camera and take up to 100 photos (make sure background is clear when doing this, nothing behind the object - move your face way)
4. Click "Train Model" and wait for training to complete
5. Test your model using your webcam or by uploading new images you haven't used before

Train Your Model

to add more categorie

؜Name of category

Accuracy is how often your model gets it right. But accuracy alone doesn't tell the whole story.

You also want to look at which categories confuse your model and ask why. The mistakes your model makes often reveal what it actually learned and what it missed. A model can be right 90% of the time while consistently failing on one specific category.

MODEL ACCURACY

EXAMPLE: TEST & REFLECT

Eagle photo → "Bird"

97% confidence

Dolphin photo → "Mammal"

 89% confidence

Bat photo → "Bird"

72% confidence

Key insight: The model learned visual patterns, not definitions.

This is how all ML works: it finds shortcuts in the data you give it.

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✓

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؜Exporting the model