Why this lesson matters
Machine learning finds patterns in examples. A system is trained using data, evaluated on data it has not seen, and then used to make predictions on new inputs. The quality of the result depends on how the problem is framed, the quality of the examples and the evaluation method.
Learning objectives
- Understand examples, features, labels, training and evaluation.
- Connect the concept to a real-world example.
- Identify one limitation or responsible-use consideration.
data, instruction or signal
rules, model or process
Key ideas
- Features are the information a model uses.
- Labels are target answers in supervised learning.
- Testing on unseen data helps estimate generalisation.
Real-world lens
When evaluating this technology, ask what problem it solves, what information it depends on, how success is measured and what happens when it is wrong. This habit is more durable than memorising product names.
Hands-on activity
Design a paper-based classifier for sorting messages into “urgent” and “not urgent.” Define three features and discuss where it could fail.
Knowledge check
Which statement best reflects responsible technology learning?
Lesson summary
Features are the information a model uses. Labels are target answers in supervised learning. Testing on unseen data helps estimate generalisation.