Supervised learning and unsupervised learning are two main types of machine learning techniques, and they work differently.

In supervised learning, you teach the model using a dataset that includes both inputs and the correct outputs (labels). Imagine you have a bunch of emails, and each email is marked as either “spam” or “not spam.” The model learns from this labeled data to understand what features (like certain words or phrases) are associated with spam. Once trained, it can then predict whether new, unseen emails are spam or not. This approach is like a teacher guiding students, providing the right answers during the learning process. Examples of supervised learning tasks are classification (sorting things into categories) and regression (predicting numerical values).

Unsupervised learning is different because the data used to train the model doesn’t come with labels. Instead, the model tries to find patterns and structures in the data independently. For instance, if you have a bunch of customer data with no labels, unsupervised learning can help group customers into segments with similar behaviours. It’s like exploring a new place without a map – you’re trying to figure out the layout based on what you observe. Common tasks include clustering (grouping similar items) and dimensionality reduction (simplifying data while keeping important parts).

In short, supervised learning uses labelled data to make predictions, while unsupervised learning finds patterns in unlabeled data.