Machine Learning (ML)

Types of Machine Learning

1. Supervised learning trains a model on labeled examples, inputs paired with the correct output, so it can predict the output for new inputs.
2. Unsupervised learning works on unlabeled data and finds structure on its own, such as clustering similar customers.
3. Semi-supervised learning combines a small labeled set with a large unlabeled one, for when labeling is expensive.
4. 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.

Related AI terms: Supervised Learning · Unsupervised Learning · Reinforcement Learning · Deep Learning · Artificial Intelligence · Online Learning

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