Machine learning systems generally learn in one of two main ways, depending on whether the training data comes with correct answers attached. Understanding this distinction makes it much easier to understand how different AI tools actually operate.
Supervised Learning
In supervised learning, the model is trained on data that already includes the correct answer, called a label. For example, a dataset of house listings paired with their actual sale prices lets a model learn to predict prices for new listings. Because the model always has a target to compare its predictions against, it can be evaluated and corrected precisely during training. Most everyday AI applications, such as spam detection and image classification, use supervised learning.
Unsupervised Learning
In unsupervised learning, the data has no labels at all, and the model must find structure on its own. A common example is customer segmentation, where a model groups similar customers together based on purchasing behaviour without being told in advance what the groups should look like. This approach is useful for discovering hidden patterns that humans might not think to look for.
There is also a middle ground called semi-supervised learning, which uses a small amount of labelled data alongside a larger pool of unlabelled data, and reinforcement learning, which learns through trial and error using rewards rather than labels. Together these approaches cover most of how modern machine learning systems are trained.