Training & Fine-tuning
Unsupervised Learning
Training where the model finds patterns in data without being given labels.
Definition
In unsupervised learning, the model is given data without being told what to look for. Instead, it discovers patterns, clusters, and structures on its own. This is how pre-training for large language models works — the model isn't given correct answers, it learns by predicting what comes next in text. Unsupervised approaches are powerful for discovering unknown patterns in large datasets, such as identifying customer segments or anomalies in transaction data.
Related Terms
Supervised Learning
Training where the model learns from labelled examples — input paired with the correct answer.
Machine Learning (ML)
A method where computers learn patterns from data rather than being explicitly programmed.
Pre-training
The initial, large-scale training phase where a model learns from massive datasets.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Training & Fine-tuning·Unsupervised Learning