Difference between supervised, unsupervised and semi supervised learning with examples and applications

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Difference between supervised, unsupervised and semi supervised learning with examples and applications
Difference between supervised, unsupervised and semi supervised learning with examples and applications



SUPERVISED
If there is a training database and you have to identify the classes within the training database, then it comes under supervised learning.

Examples
1.     Check if a particular image is of a face or of a car.
2.     Check if an animal is mammal or reptile
3.     Predicting the weather.
4.     Bio-metric attendance.
5.     Given information about the bungalow, predict the price.
6.     Netflix – Given a user and movie, predict the rating of the movie.
7.     Finding images which contains human faces- given a database for training
8.     Identifying number plate of a car, given a database of pictures and numbers.
9.     Email is spam or not.
10. Identify the patient is ill or not.
11. Predict stock market price.
12. Predicting which batch of food will taste bad based on factory temperature, humidity and previous taste tester data.
13. Identify if a news is related to politics, sports etc.

UNSUPERVISED
If there are no classes and you have to group something without having a training database, then unsupervised learning is used. 

Examples

1.     Grouping the pixels by color in an image.
2.     Given a news report, group similar news together.
3.     Find fraudulent credit card transactions.
4.     Find a yellow circle in a video without having any samples of the video.
5.     Find the most effective hashtag for more likes on Instagram.

 SEMI SUPERVISED
If there is a training database and you have to identify different classes other than from the training database, then it comes under semi supervised learning.

Examples

1.     Given a training data set of Ships, Identify tank in a picture.
2.     Speech analysis.
3.     Web content classification.
4.     Find cars, scooters, and bicycles in an image given the training data set with only bicycles and cars.
Usually classification and regression are considered as supervised learning.
Clustering and association are unsupervised learning.


Also learn the  Difference between Classification, Clustering and Regression with examples and applications.