What is Unsupervised Machine Learning?
Unsupervised machine learning is a type of machine learning where algorithms learn from data that has no pre-defined labels or categories. In contrast to supervised learning where the training data is labeled (think “cat” pictures and “dog” pictures), unsupervised learning algorithms are tasked with finding hidden patterns or structures within the data itself.
Unsupervised Machine Learning Examples
Unsupervised machine learning represents a pivotal domain within artificial intelligence, emphasizing the extraction of patterns and structures from data devoid of prior labeling. Unlike its supervised counterpart, which relies on labeled outcomes to guide predictions, unsupervised algorithms delve into the intrinsic characteristics of data to discern similarities, differences, and underlying patterns. This approach fosters autonomy in data analysis, allowing algorithms to uncover latent insights and segment data without human intervention.
This article explores how Unsupervised Machine Learning Examples, provides examples across various domains, and answers frequently asked questions about its applications.