Unsupervised Learning#
Unsupervised learning is a type of machine learning in which a model is trained on unlabeled data, meaning that the correct output is not provided for each example in the training data. The goal of unsupervised learning is to discover underlying patterns and structure in the data, without the use of any predefined labels or categories. This allows the model to learn from the data in a more flexible and open-ended way, and it can be used to perform tasks such as clustering, dimensionality reduction, and density estimation. Examples of unsupervised learning algorithms include k-means clustering and principal component analysis.