Introduction to Unsupervised learning

Unsupervised learning is a type of machine learning where the goal is to discover patterns or relationships in the data without the use of labeled examples. In contrast to supervised learning, where the machine learning model is provided with labeled examples and learns to make predictions based on those examples, unsupervised learning is used when there are no predefined labels or targets to predict.

In unsupervised learning, the machine learning algorithm is presented with a dataset and is tasked with finding hidden patterns or structures within the data. The algorithm identifies similarities and differences between different data points and groups them based on these similarities and differences. Unsupervised learning can be used for tasks such as clustering, dimensionality reduction, and anomaly detection.

Clustering is a common application of unsupervised learning, where the goal is to group similar data points together into clusters. Dimensionality reduction involves reducing the number of variables or features in a dataset while still retaining as much information as possible. Anomaly detection involves identifying data points that are significantly different from the majority of the data.

Unsupervised learning algorithms are widely used in various fields such as computer vision, natural language processing, finance, and healthcare. Some common examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA), and autoencoders.