Supervised and Unsupervised learning

Supervised Learning :

It is a type of Machine Learning where the system takes a labeled data as an input and make predicts new data by learning through patterns & Algorithms.

It predicts the next data from learning the previous patterns and the labeling

We Can Visualise it by a simple diagram:

Un-Supervised Learning:

It is a type of Macine Learning where the sytem takes an un labelled data as an input and learns the data with the help of patterns, but do not give labelled data as an output.

It clusters/groups the data by learning from the previous patterns.

We can take an Un-supervised Learning:

What are the main key diferences Between them :

See, the processes In both the methods are the same It takes data, cleans, noramlizes tranforms it hten it send it to modeling the difference comes when the model starts getting validated in supervised learning the model gets validated or we can say the model tested which carries the accuracy, F1-score, recall or ROC curve while in the un-supervised learning the model do not get validated it is because un-supervised learning takes un-labelled data as an input and it simply visualizes through patterns istead of this it gets clustered or arranges the data