In plain words
Machine learning is a part of AI in which a system’s behavior is learned from examples or feedback instead of being specified entirely through hand-written rules.
A closer look
Supervised learning uses example inputs with target outputs. Unsupervised methods find useful structure without those task labels. Self-supervised learning constructs targets from the data itself, such as predicting hidden or subsequent text. Reinforcement learning uses rewards associated with actions or outcomes.
The central challenge is generalization: performing well on new cases, not just familiar training examples. Engineers separate training and evaluation data to estimate this ability. Models can overfit, learning details that work in the training set but fail elsewhere, or face changing conditions after deployment.
In practice
Rather than manually listing every phrase that indicates spam, a team trains a classifier on labeled messages. The resulting model learns patterns it can apply to new mail.
A useful distinction
A deployed machine-learning system does not necessarily keep learning from every interaction. Many models remain fixed between scheduled training runs, even when an application stores new data or remembers user preferences.