What is the difference between supervised and unsupervised learning tasks?

Supervised (training with labels) and unsupervised (training without labels) are the two main types of machine learning.

• Supervised learning: the model is trained on labeled data, where each input has a corresponding correct answer (label). The goal is to learn to predict labels for new data. Example: classifying emails as spam or not spam.

• Unsupervised learning: the model works with unlabeled data, trying to find hidden structures or patterns without pre-known answers. Example: clustering customers based on behavior.

The key difference is the presence or absence of labels in the training data.