What is Machine learning?

Software that learns patterns from data instead of following rules written by hand.

In plain English

You give an algorithm many examples, it finds patterns, and the result is a model that can make predictions on new data. Supervised learning learns from labelled examples, and unsupervised learning looks for structure in unlabelled data. How well a model works depends heavily on the quality of the data.

Example

A model trained on thousands of labelled emails learns to predict whether a new email is spam.

Easy to confuse with

• Deep learning: Deep learning is one branch of machine learning that uses many-layered neural networks.

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 We do not publish or predict real exam questions.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
• ISACA AI Fundamentals practice tests
• Microsoft AI-901 practice tests

Related terms

• Supervised vs unsupervised learning: Supervised learning trains on labelled examples. Unsupervised learning looks for structure in unlabelled data.
• Neural network: A model made of layers of simple connected units that learns by adjusting the strength of the connections.
• Overfitting: When a model learns its training data too closely and does badly on new data.

Written 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.