A subfield of AI that develops algorithms allowing machines to learn patterns from data and make predictions or decisions — without being explicitly programmed for each scenario.
In Depth
Machine Learning is the engine behind most modern AI applications. Instead of a programmer writing explicit rules — for example, marking as spam any email containing the word lottery — an ML system is given thousands of examples of spam and legitimate email, and learns to distinguish them on its own. The system extracts statistical patterns from data and builds an internal model that generalizes to new, unseen examples.
ML can be broken into three main paradigms. In Supervised Learning, the model learns from labeled examples (input-output pairs). In Unsupervised Learning, the model finds hidden structure in unlabeled data — grouping similar customers together, for instance. In Reinforcement Learning, an agent learns by interacting with an environment and receiving rewards or penalties based on its actions.
The quality and quantity of training data are the single greatest determinants of ML model performance. A powerful algorithm trained on poor data will produce poor results — a principle sometimes summarized as 'garbage in, garbage out.' Feature Engineering, Cross-Validation, and Hyperparameter Tuning are the core practices that transform raw data and algorithms into reliable, deployable systems.
Machine Learning replaces hand-coded rules with data-driven pattern recognition — making it possible to automate tasks that are too complex or variable to program explicitly.
Real-World Applications
Frequently Asked Questions
How does Machine Learning work?
Machine Learning works by feeding data to an algorithm that identifies patterns and relationships. The algorithm adjusts its internal parameters to minimize prediction errors, improving with each iteration. Once trained, the model can apply these learned patterns to new, unseen data to make predictions or decisions — without having been explicitly programmed for each scenario.
What are the three main types of Machine Learning?
The three main types are: Supervised Learning (the model learns from labeled examples, like emails tagged as spam or not spam); Unsupervised Learning (the model discovers hidden patterns in unlabeled data, like customer segments); and Reinforcement Learning (an agent learns through trial and error by receiving rewards for good actions and penalties for bad ones).
What is the difference between Machine Learning and AI?
AI is the broader goal of creating machines that simulate human intelligence. Machine Learning is one approach to achieving AI, specifically through algorithms that learn from data. Other AI approaches include rule-based expert systems and symbolic reasoning. Today, ML is the most successful and widely used approach to building AI systems.