---
title: "Machine Learning Basics"
description: "A basic explanation of machine learning: a computer studies many examples, finds patterns and makes predictions about new things. Includes hands-on practice with Teachable Machine or a similar tool."
canonical: https://lightmysky.com/learn/computing/machine-learning-basics-mt_K6qtan847r
source: https://lightmysky.com/learn/computing/machine-learning-basics-mt_K6qtan847r.md
retrieved: 2026-10-19
---

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# Machine Learning Basics

A basic explanation of machine learning: a computer studies many examples, finds patterns and makes predictions about new things. Includes hands-on practice with Teachable Machine or a similar tool.

Subject: Computing · Area: Artificial Intelligence · Ages 7 to 9
Page: https://lightmysky.com/learn/computing/machine-learning-basics-mt_K6qtan847r

## Ready when they can

- 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 how relevant, varied examples can help a model and why its guesses still need testing

## Lesson: Teaching a computer to notice things

You have sorted things by a rule. Machine learning lets a computer learn patterns from examples. People write the learning software. For our picture sorter, first give labeled examples, then train it to find clues that separate the groups. Next, try a new picture and check its guess. A guess is called a prediction.

*(drawing: Training and checking are different jobs. The new picture is not one used for training.)*

**Example.** Nora uses Teachable Machine to sort sneakers and rain boots. She supplies labeled photos, selects Train, then checks new photos. A guess matching the known label is correct; a different label is a mistake. The model might use shape or background clues. To compare results, subtract the smaller number of correct guesses from the larger: 8 minus 6 is 2 more.

*(drawing: In this made-up test, A got 6 right and B got 8 right out of 10. Results depend on the training examples and the test pictures.)*

Useful examples show different shoes, angles, light and backgrounds. Repeating one photo adds little variety. If boot photos are rainy and sneaker photos are dry, a model may learn rain as a clue. Show both kinds in different weather. More relevant, varied examples can help, but test new pictures to find out.

**Recap.** Give examples, train the model, then check its guesses on new examples. Relevant variety can help it learn useful patterns.

## Practice

8 questions on this page, each with its working shown.

## Needs first

- [Patterns and Classification](https://lightmysky.com/learn/computing/patterns-and-classification-mt_ofOGCQ7FWj)

## Opens up

- [Joint, Marginal and Conditional Probability](https://lightmysky.com/learn/computing/joint-marginal-and-conditional-probability-mt__Rj-hOSMEw)
- [How a Chatbot Picks Its Next Token](https://lightmysky.com/learn/computing/how-a-chatbot-picks-its-next-token-mt_1Zbhamhzai)
- [Weighted Models: Learned Parameters and Written Code](https://lightmysky.com/learn/computing/weighted-models-learned-parameters-and-written-code-mt_3_1toZUxak)
- [AI Mistakes and Limitations](https://lightmysky.com/learn/computing/ai-mistakes-and-limitations-mt_bPFToj0OhZ)
- [Humans Versus Machines](https://lightmysky.com/learn/computing/humans-versus-machines-mt_oajUvqAiBJ)
- [Deepfakes and AI-Generated Content](https://lightmysky.com/learn/computing/deepfakes-and-ai-generated-content-mt_tzMr83pS8v)
- [AI and the Environment](https://lightmysky.com/learn/computing/ai-and-the-environment-mt_ZpCcTU8j_o)
