Unsupervised Learning

Unsupervised learning is a type of machine learning that learns from unlabeled data. This means that the data does not have any pre-existing labels or categories. The goal of unsupervised learning is to discover patterns and relationships in the data without any explicit guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data. Therefor the machine is restricted to find the hidden structure in unlabeled data by itself. In unsupervised learning, the objective function often needs to be relatively sophisticated.

Types of Unsupervised Learning

Unsupervised learning is classified into two categories of algorithms: 

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. more…

• Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. more…

• Dimensionality Reduction: Dimensionality reduction is a technique used in machine learning and statistics to reduce the number of features or variables in a dataset. The main goal is to simplify the dataset by retaining its essential information while eliminating irrelevant or redundant features. more..