What is an unsupervised learning?

Unsupervised learning is similarly a simple learning model, but it lacks a critic and no way to check its success, as the name implies. The goal is to create a mapping function that classifies data based on hidden properties.

Unsupervised learning is divided into two parts, just like supervised learning. The mapping function break data collection into courses in the first step . Although each input vector is given a course, the algorithm cannot assign labels to those classes.

The result may be data segmentation into classes (from which you may then conclude the resulting courses). Still, you can use these classes in other ways depending on the application.

A recommendation system is one such application in which the input vector represents a user’s traits or purchases. Users within a class represent similar interests that Users can then utilize for marketing or product recommendations.

You can use various methods to achieve unsupervised learning , such as k-means clustering, adaptive resonance theory, or ART .