Data & AI
Machine learning is a branch of artificial intelligence in which systems learn to make predictions or decisions by identifying patterns in data, rather than being explicitly programmed with rules.
Machine learning algorithms are trained on labelled or unlabelled datasets to build statistical models that generalise to new, unseen inputs — enabling applications like image recognition, fraud detection, demand forecasting, and product recommendations. The three primary paradigms are supervised learning (training on labelled input-output pairs), unsupervised learning (finding hidden structure in unlabelled data), and reinforcement learning (learning optimal actions through reward signals from an environment). Model performance depends critically on data quality, volume, and relevance, which is why data engineering and feature engineering are as important as algorithm selection. After training, ML models are deployed as APIs or embedded in applications where they make real-time or batch predictions on production data.
Example
An insurance company trains a supervised machine learning model on 10 years of claims data to predict the probability of fraud for each new claim, flagging high-risk cases for human review.
Related terms
Artificial Intelligence
Artificial intelligence is the field of computer science concerned with building systems that can perform tasks requiring human-like intelligence — such as reasoning, language understanding, visual perception, and decision-making.
LLM (Large Language Model)
An LLM is a type of AI model trained on massive text datasets to understand and generate human language, capable of tasks such as question answering, summarisation, code generation, and conversational dialogue.
Data Warehouse
A data warehouse is a centralised, integrated repository of structured historical data from multiple operational systems, optimised for analytical queries and business intelligence reporting rather than transactional processing.
ETL (Extract, Transform, Load)
ETL is a data integration process in which data is extracted from one or more source systems, transformed into a consistent and clean format, and loaded into a destination system such as a data warehouse for analysis.
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