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
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Teaching Table · Plan outline
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Family records and proof options
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Save photos, notes, and attendance. Export a proof packet for compliance or portfolio reviews.
Learning resources
Reviewed external links that reinforce this concept. They do not replace Gakuva’s teaching approach.
Google Creative Lab · Practice link — not the full lesson
Teachable Machine · Image
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
Review and open practice
Save 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.
Open practice (new tab)Source, access and review notes
Hands-on machine-learning basics: gather examples, train, and test. Emphasize that the computer only learns from the examples you give it.
Save before/after screenshots (classes filled → prediction) and a parent note in the learner’s words: “It learned from my examples.”
Free · no login · no ads observed · webcam optional
Unknown — link only
Code.org · Practice link — not the full lesson
AI for Oceans
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
Review and open practice
Save 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.
Open practice (new tab)Source, access and review notes
Guided story-mode introduction to training data and model testing for younger learners before open-ended Teachable Machine work.
Save a progress screenshot and a parent note explaining training data vs a later test in kid language.
Free · no login required to play · account optional to save · no ads observed
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