Resources & Downloads

Download all the files you need for this unit. Click the buttons below to save each file to your computer.

Downloads

Unit Pack

Full scheme of work + all 6 student worksheets (DOCX)

Download DOCX

Student Help Sheet

Step-by-step guide to using Teachable Machine (DOCX)

Download DOCX

Starter Slide Deck

PowerPoint slides for lesson starters (PPTX)

Download PPTX

Demo Datasets

Pre-made training & testing images (ZIP)

Download ZIP

How to Set Up Your Files

Step 1: Create Your Project Folder

  1. On your desktop, right-click and choose New > Folder.
  2. Name the folder TeachableMachine_Unit.
  3. Download all the files above and save them into this folder.

Step 2: Unzip the Dataset

  1. Find the TeachableMachine_Demo_Datasets.zip file in your folder.
  2. Right-click on it and choose "Extract All..."
  3. Click Extract. This will create a new folder with all the image files inside.
  4. Inside you should see folders organised by lesson and shape type.

Step 3: Understand the Dataset Structure

The demo dataset is organised like this:

TeachableMachine_Demo_Datasets/
├── Balanced_Dataset/
│   ├── TRAIN/
│   │   ├── Circles/      (10+ images)
│   │   ├── Triangles/    (10+ images)
│   │   └── Squares/      (10+ images)
│   └── TEST/
│       ├── Circles/      (5 images)
│       ├── Triangles/    (5 images)
│       └── Squares/      (5 images)
│
└── Biased_Dataset/
    ├── Circles_YellowBG/ (circles on yellow)
    └── Triangles_BlueBG/ (triangles on blue)
    

About the Datasets

Balanced Dataset vs Biased Dataset

Balanced dataset: Both classes have the same number of images, and all images use the same white background. The model has to learn the shape itself.

Biased dataset: Circles are on a yellow background and triangles are on a blue background. The model might learn the background colour instead of the shape — this is the "background trick" used in Lesson 2.

TRAIN vs TEST — Why It Matters

TRAIN folder: These images are used to teach the model. You upload them into Teachable Machine's classes.

TEST folder: These images are used to check the model. They must be images the model has never seen before. This tells you how the model will perform on new, real-world data.

If you test with training images, you might get 100% accuracy — but that doesn't mean the model actually learned. It might have just memorised. Always test with fresh images!

Teacher Notes: Managing Resources