What is Supervised vs unsupervised learning?
Supervised learning trains on labelled examples. Unsupervised learning looks for structure in unlabelled data.
In plain English
In supervised learning each training example comes with the right answer, so the model learns to predict it. Classification (a category) and regression (a number) are the two usual tasks. In unsupervised learning there are no answers given, and the model finds patterns itself, such as clusters of similar customers.
Example
Predicting house prices from past sales is supervised. Grouping customers by buying behaviour without predefined groups is unsupervised.
Where you will meet it
This idea belongs to the subject area of these practice tests. We do not publish or predict real exam questions.
- AWS AIF-C01 practice tests
- AWS MLA-C02 practice tests
- Microsoft AI-901 practice tests
- NVIDIA NCA-AIIO practice tests
Related terms
- Overfitting: When a model learns its training data too closely and does badly on new data.
- Model drift: A model getting worse over time because the real world no longer looks like its training data.
Test yourself
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Browse practice testsAll glossary termsWritten and checked by the Be Ready Education team, last reviewed 2 November 2026. General study information, not professional advice. Providers change names and rules, so confirm details on the official documentation. Not affiliated with, endorsed by or sponsored by any certification vendor or exam body. Certification names, exam codes and product names are trademarks of their respective owners and are used only to identify the exam a practice product prepares for. Our questions are original and are not actual exam questions.