Unsupervised learning is a type of machine learning where the model is not given any labeled training data or feedback on its performance. Instead, it’s given a dataset and is asked to learn patterns and relationships within the data on its own. This is in contrast to supervised learning, where the model is given labeled training examples and is trained to make predictions based on those examples. Unsupervised learning can be used for a variety of tasks, such as clustering data points into groups, detecting anomalies or outliers in the data, and finding hidden patterns or relationships within the data.
