A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning algorithms build mathematical models based on sample data (known as “training data”) to make predictions or decisions without requiring explicit instructions for each scenario.

Machine learning uses statistical techniques to enable computers to improve their performance on a specific task through experience. This approach differs from traditional programming, where explicit rules must be provided for every situation. Instead, ML systems develop their own rules by analysing patterns in data. The quality and representativeness of the training data significantly influence the effectiveness of ML models, making data collection and preparation critical steps in the ML development process.

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A fraud detection system for financial transactions that learns from historical transaction data to identify unusual patterns and flag potentially fraudulent activities, becoming more accurate over time as it processes more examples.