Machine Learning (ML)
Last Updated: September 10, 2026 | By Mihail Sebastian | AI Dictionary
A field of AI where systems learn patterns from data and improve with experience, rather than following rules a programmer wrote by hand for every case.
What is Machine Learning (ML)?
Machine learning is the branch of artificial intelligence in which systems learn patterns from data and improve with experience, instead of following rules a programmer wrote by hand.
The difference is who writes the logic. In conventional software a person specifies every rule; in machine learning, an algorithm works the rules out from examples. That is why the same technique can filter spam, read X-rays, or price houses.
Types of Machine Learning
- Supervised learning trains a model on labeled examples, inputs paired with the correct output, so it can predict the output for new inputs.
- Unsupervised learning works on unlabeled data and finds structure on its own, such as clustering similar customers.
- Semi-supervised learning combines a small labeled set with a large unlabeled one, for when labeling is expensive.
- Reinforcement learning lets an agent learn by trial and error, guided by rewards and penalties from its environment.
Two related terms sit alongside these. Deep learning is not a fifth paradigm but a family of neural-network techniques used within all four, and self-supervised learning generates labels from the raw data itself – the approach behind most large language models.
Example of Machine Learning
A spam filter is machine learning end to end. Engineers collect thousands of emails already marked spam or legitimate, and a supervised algorithm learns which signals separate the two: sender reputation, suspicious links, telltale phrasing.
The trained model then scores every new email. When spammers change tactics and users flag messages the filter missed, those flags become fresh training data and the filter retrains. The system improves from experience without anyone rewriting its rules.
For a hands-on introduction, our step-by-step machine learning tutorial walks through training a first model.
FAQ
What is machine learning used for?
Most machine learning runs invisibly inside products you already use: spam filters, fraud and credit scoring, product and content recommendations, speech recognition, language translation, and medical image analysis. The common thread is a task with many examples and patterns too subtle or too numerous to write as rules by hand.
Is machine learning the same as AI?
No. Artificial intelligence is the broader goal of machines performing tasks that need intelligence; machine learning is the family of techniques that gets there by learning from data. Nearly all modern AI is built on machine learning, but AI also includes older rule-based approaches like expert systems.
Related AI terms: Supervised Learning · Unsupervised Learning · Reinforcement Learning · Deep Learning · Artificial Intelligence · Online Learning
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