Machine Learning

Machine Learning (ML) is a subfield of AI in which systems learn statistical patterns from data rather than being explicitly programmed with rules.

What is Machine Learning?

Machine Learning algorithms include linear and logistic regression, decision trees, random forests, gradient-boosted trees, support vector machines, and neural networks. ML projects typically involve a labelled training set, a held-out validation set, and an evaluation harness measuring accuracy, precision, recall, F1, or business-specific metrics. ML systems can be supervised (trained on labelled examples), unsupervised (find structure in unlabelled data), or reinforcement-based (learn from rewards). Most enterprise ML in production is supervised classification or regression on tabular or text data.

How does Machine Learning apply to enterprise AI?

Enterprises use ML for credit scoring, claims triage, churn prediction, document classification, and anomaly detection. Under the EU AI Act, many such systems qualify as high-risk and require conformity assessment, data governance, and post-market monitoring.