Training & Fine-tuning

Supervised Learning

Training where the model learns from labelled examples — input paired with the correct answer.

Definition

Supervised learning is the most common form of AI training for business applications. You provide the model with a set of examples where both the input and the correct output are known: emails labelled as spam or not-spam, invoices paired with their extracted data, customer messages paired with the correct category. The model learns to generalise from these examples to new cases. The quality and size of your labelled dataset directly determines how well the model performs.

Related Terms

Unsupervised Learning

Training where the model finds patterns in data without being given labels.

Fine-tuning

Further training a pre-trained model on specific data to specialise it for a task.

Training Data

The text, documents, or other content an AI learns from during its development.

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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·Supervised Learning