What is unsupervised learning in AI?
Unsupervised learning is a type of machine learning where the model learns from data without any explicit labels or outcomes. The goal is to discover hidden patterns or structures within the data. Common techniques include clustering, where the model groups similar data points together, and dimensionality reduction, where the model identifies the most important features of the data.
How does unsupervised learning work?
Unsupervised learning works by identifying patterns in the data based on the data's inherent characteristics. For example, in clustering, the model might group data points based on their proximity to each other in the feature space. In dimensionality reduction, the model might identify the features that account for the most variance in the data.
Unsupervised learning can be a powerful tool for exploratory data analysis, as it can reveal unexpected patterns or structures in the data.
What are the applications of unsupervised learning?
Unsupervised learning is used in a variety of applications, including anomaly detection, where the model identifies data points that deviate from the norm; recommendation systems, where the model identifies items that are similar to those a user has liked in the past; and data visualization, where dimensionality reduction techniques can be used to visualize high-dimensional data in two or three dimensions.