Unsupervised learning is a branch of machine learning in statistics that involves discovering patterns or structures in data without the need for labeled responses. Unlike supervised learning, where the algorithm is given explicit instructions on how to categorize data, unsupervised learning algorithms must uncover patterns on their own.

Category : Machine Learning in Statistics en | Sub Category : Unsupervised Learning Posted on 2023-07-07 21:24:53

Unsupervised learning is a branch of machine learning in statistics that involves discovering patterns or structures in data without the need for labeled responses. Unlike supervised learning, where the algorithm is given explicit instructions on how to categorize data, unsupervised learning algorithms must uncover patterns on their own.

Clustering and dimensionality reduction are two common tasks performed using unsupervised learning techniques. Clustering involves grouping similar data points together based on some similarity measure, while dimensionality reduction aims to reduce the number of features in a dataset while preserving the most important information.

One popular unsupervised learning algorithm is k-means clustering, which partitions a dataset into k clusters based on the similarity of data points to each cluster's centroid. Another well-known technique is principal component analysis (PCA), which identifies the most important dimensions in a dataset and projects the data onto a lower-dimensional space.

Unsupervised learning is widely used in various applications, such as customer segmentation, anomaly detection, and recommendation systems. By identifying hidden patterns in data, unsupervised learning algorithms can provide valuable insights and drive decision-making in a variety of fields.