Un-Supervised Machine Learning

 

2) Un-Supervised Machine Learning.

In data, there are only inputs and no outputs, which are called unlabeled data. When we identify patterns or groups in the data without any output, it is called Unsupervised Machine Learning.

When data has only input and no output, it is called unsupervised machine learning.

Example: Student data

Input
IQ	CGPA
110	8.5
120	9.1
100	7.8
115	8.8
105	8.0

In Un-Supervised Learning, there is no output—only input data. So, we perform tasks such as Clustering, Dimensionality Reduction, Anomaly Detection, and Association Rule Learning.

2.1      Types of Un-Supervised Machine Learning:

I.      Clustering :

Clustering is an unsupervised machine learning technique that groups similar data points into clusters based on their features, without using any labeled data.

 For example,

we have a dataset of IQ and CGPA. We plot this data on a 2D coordinate system, where the X-axis represents IQ and the Y-axis represents CGPA. A clustering algorithm detects groups of students such as high IQ–high CGPA, high IQ–low CGPA, low IQ–high CGPA, and low IQ–low CGPA. In this way, students are grouped into categories. When a new student comes, the algorithm places the student into a group, and we can assign labels like 1, 2, 3, or 4 to the groups.