Machine Learning
Quick Answer
Machine Learning (ML) is a subfield of AI in which systems learn patterns from historical data rather than following explicitly programmed rules, enabling them to make predictions or decisions on new, unseen data as conditions evolve.
In Depth
What Machine Learning really means
Machine Learning algorithms iteratively adjust their internal parameters to minimise error on a training dataset, gradually improving their accuracy. The three dominant paradigms are supervised learning (learning from labelled examples), unsupervised learning (finding structure in unlabelled data), and reinforcement learning (learning by trial and error).
ML powers most modern AI products, including spam filters, recommender systems, demand forecasting engines and credit scoring models. Crucially, ML systems can degrade over time if the real-world data distribution drifts, which is why ongoing monitoring is essential.
Why It Matters
Business relevance for UK organisations
UK organisations apply ML to problems where historical data is plentiful and the underlying patterns are stable enough to learn. Common use cases include churn prediction, inventory forecasting, pricing optimisation and personalised recommendations.
Real-world example
How this shows up in practice
A London e-commerce retailer uses machine learning to forecast weekly demand per SKU, reducing stockouts by 31% and surplus inventory by 18%.
Related Terms
Continue exploring
Supervised Learning
Supervised learning is a machine learning approach in which the model is trained on a dataset containing inputs paired with their correct outputs (labels). The model learns to map inputs to outputs, enabling it to predict labels for new, unseen examples.
BasicsUnsupervised Learning
Unsupervised learning is a machine learning approach where the model learns patterns and structure from unlabelled data. Rather than predicting a known target, it uncovers groupings, anomalies or compressed representations hidden in the data.
BasicsTraining Data
Training data is the dataset used to teach a machine learning model the patterns it needs to perform its task. The quality, quantity, diversity and recency of training data directly determine how accurate and fair the resulting model will be.
AdvancedMLOps
MLOps is the discipline of operationalising machine learning: the practices, tools and culture needed to deploy, monitor, retrain and govern models reliably in production. It extends DevOps thinking to the unique challenges of data and models.
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