Machine Learning Basics
How machine learning works at a conceptual level: show the computer many examples, it finds patterns, then it makes predictions about new things; hands-on experience with Teachable Machine or similar tool
Lesson
Set out your lesson
“Machine Learning Basics means: show many examples, the system finds patterns, then it predicts about something new. We will train a human “model” with cards, test a new card, talk about mistakes, then check a fresh prediction.”
Bring to the table
- Paper cards: many circles vs triangles (or sock vs spoon photos)
- Optional: adult-supervised Teachable Machine in a browser later—not required for the table lesson
Extra for the adult
Worked example. After many circle examples, a new round circle is predicted “circle.” A weird oval might be mislabeled—that is a model mistake.
Optional challenge. For Machine Learning Basics, deliberately train with biased examples (only red circles) and show how predictions fail on a blue circle. Optional: adult-led Teachable Machine image project another day.
What to notice. Keep one parent note: train examples; predictions; mistake talk; support used. Optional tool use is extra, not required evidence. This session samples the concept, not mastery of ML tools.
Share and support
Family records and proof options
Keep a record of your learning
Save photos, notes, and attendance. Export a proof packet for compliance or portfolio reviews.
Practice this concept
Start practiceMachine learning basics practice
Use examples to predict, and expect mistakes.
Free · no login · no ads · Practice answers and completion are not saved. Parent observation remains the lesson record.
Learning resources
Extra practice links — they do not replace Gakuva’s teaching approach.
Google Creative Lab · Practice link — not the full lesson
Teachable Machine · Image
Before you open · source notes
Hands-on machine-learning basics: gather examples, train, and test. Emphasize that the computer only learns from the examples you give it.
Free · no login · no ads observed · webcam optional
Before you openSave before/after screenshots (classes filled → prediction) and a parent note in the learner’s words: “It learned from my examples.”
Opening this link does not save learning or assess mastery.
Source, access and review notes
Unknown — link only
Code.org · Practice link — not the full lesson
AI for Oceans
Before you open · source notes
Guided story-mode introduction to training data and model testing for younger learners before open-ended Teachable Machine work.
Free · no login required to play · account optional to save · no ads observed
Before you openSave a progress screenshot and a parent note explaining training data vs a later test in kid language.
Opening this link does not save learning or assess mastery.
Source, access and review notes
Unknown — link only
Check understanding
- Describe the three steps of machine learning in simple terms (give examples, find patterns, make predictions)
- Train a simple model using a tool like Teachable Machine and describe what happened
- Explain why showing more and better examples makes the model more accurate
“Could your child describe how you teach a computer to recognise something — by showing it lots of examples until it spots the pattern?”
Curriculum record
- Type
- Conceptual
- Subject
- Computing
- Domain
- Artificial Intelligence
- Age range
- Ages 7–9