Unsupervised Learning

Unsupervised learning is a type of ML where the algorithm is trained on an unlabeled dataset, meaning that the data has no predefined labels or categories. The goal of unsupervised learning is to identify patterns or structures within the data.

Unsupervised learning is used when it's impossible to use supervised learning techniques. It's often used in data mining, exploratory data analysis, and anomaly detection.

However, because unsupervised learning algorithms have no specific goals or objectives, they can be more difficult to evaluate and optimize than supervised learning algorithms. As such, unsupervised learning is often used in conjunction with supervised learning to improve the performance of ML models.