Supervised and Unsupervised Machine Learning

In “Supervised Machine Learning”, we will have something called “labels”.

Ex:

Predict the score of a student based on their 10th std marks, 12th std marks etc here we have label as “score of a student”.

In “Unsupervised Machine Learning”, we don’t have something called “labels”.

Ex:

In social media, Cluster Analysis is used to identify similar communities within larger groups.

Grouping customers of a store into different clusters and making a separate targeted marketing strategy for each group.

As I mentioned in my previous post, types of ML algorithms are Regression, Classification and Clustering. Regression & Classification are supervised machine learning and Clustering is unsupervised machine learning.

Classification and Clustering will be more or less same. But main difference between them is labels are used in Classification whereas labels are not used in Clustering.

Some more examples to better understand Supervised/Unsupervised:

1. We have the data of past 5 years of two football teams on the performance of the teams based on different parameters and the match results. We have to predict which team will win - This is Supervised since we have past data to train our model.
2. We have a large collection of emails to the learning model to identify the different sub-groups of these mails. No labels are presents in the data set - This is Unsupervised since we don’t have the data.

In next blog, you will see what is Regression and Types of Regression with Example: https://raghulprasanthpsg.medium.com/regression-a66e6c0ba992

In previous blog, you can see what is Machine Learning and Introduction: https://raghulprasanthpsg.medium.com/introduction-to-machine-learning-dd2bba5e511