What is Machine Learning?
How computers learn from data instead of being explicitly programmed.
Machine Learning (ML) is a branch of AI where computers learn from examples rather than being told every single rule.
Imagine teaching someone to recognise cats. Instead of listing every rule ("has whiskers, has four legs…"), you show them thousands of cat photos. Eventually they spot the pattern themselves. Machine learning works similarly — the computer finds patterns in data.
This is why ML powers so many modern AI tools. The more relevant data a system has, the better it can become at its task — whether that is translating languages, detecting fraud, or suggesting what to write next.
Examples
- •Email spam filters that improve as they see more messages
- •Streaming services recommending shows based on what you watched
- •Banks flagging unusual transactions as potential fraud
- •Voice assistants learning to understand different accents over time
How machine learning works (simplified)
- 1
Collect examples
Gather data — photos, emails, sales records, or text.
- 2
Find patterns
The system looks for patterns in that data automatically.
- 3
Apply patterns
When new data arrives, it uses those patterns to make predictions or decisions.
Key points
- ✓Machine learning means computers learn from data, not fixed rules
- ✓Systems improve as they process more relevant examples
- ✓ML is a key technique behind many modern AI tools
- ✓You do not need to understand the maths to grasp the concept
Knowledge check
How does machine learning differ from traditional programming?
Choose the best answer, then check your understanding.