Glossary · 1 · Foundations: how AI works
Machine learning (ML)
Also known as: ML
German: Maschinelles Lernen
Machine learning (ML) is the approach to artificial intelligence in which a system learns patterns from example data and uses them to make predictions or decisions on new data, instead of being programmed with explicit rules for every case.
- Beginner
- Technical writers
- Technical marketers
- Technical project managers
In one sentence
Machine learning explained for beginners: systems that learn patterns from example data instead of following hand-written rules.
Example
A spam filter trained on thousands of labeled emails learns which features signal spam and applies them to messages it has never seen.
Explained in context
Context cards connect this term with others to answer one question. Also in British English and German.
Why it matters on your learning path
- Technical writers: Most AI features in authoring and delivery tools — auto-tagging, search ranking, translation — are machine learning. Their output reflects the data they learned from, including your content.
- Technical marketers: Machine learning is a mature, broad term; use it when a feature predicts or classifies rather than generates.
- Technical project managers: ML projects depend on data: its availability, quality and rights of use are schedule risks, not only technical details.
Machine learning vs. rule-based software
A rule-based system does exactly what its rules say and can be traced line by line. A machine learning system behaves according to what it learned, which makes it flexible but harder to explain — the reason evaluation and human in the loop review matter.