Unsupervised Machine Learning

In simple term unsupervised machine learning is defined by the model without having any labeled datasets. Instead, the model finds patterns for discovering useful information for the given real-world datasets.

 

Why Unsupervised Learning?

Here, are the most important reasons for using Unsupervised Learning:

• Unsupervised machine learning finds all kind of unknown or hidden patterns in the given data.
• Unsupervised algorithms help you to find features which can be useful for categorization.
• It is taken place in real-time datasets, so all the input data to be analyzed and labeled in the presence of learners.

 

Types of Unsupervised Learning

There are mainly three different types of clustering you can utilize:

Clustering Types

• K-means clustering
• Principal Component Analysis
• Hierarchical clustering