Can Anyone Give A Real Life Example Of Supervised Learning And Unsupervised Learning?

Answer :

Supervised learning:

• You get a bunch of photos with information about what is on them and then you train a model to recognize new photos.
• You have a bunch of molecules and information about which are drugs and you train a model to answer whether a new molecule is also a drug.

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Unsupervised learning:

• You have a bunch of photos of 6 people but without information about who is on which one and you want to divide this dataset into 6 piles, each with the photos of one individual.
• You have molecules, part of them are drugs and part are not but you do not know which are which and you want the algorithm to discover the drugs.

Supervised Learning:

• is like learning with a teacher
• training dataset is like a teacher
• the training dataset is used to train the machine

Example:

Classification: Machine is trained to classify something into some class.

• classifying whether a patient has disease or not
• classifying whether an email is spam or not

Regression: Machine is trained to predict some value like price, weight or height.

• predicting house/property price
• predicting stock market price

Unsupervised Learning:

• is like learning without a teacher
• the machine learns through observation & find structures in data

Example:

Clustering: A clustering problem is where you want to discover the inherent groupings in the data

• such as grouping customers by purchasing behavior

Association: An association rule learning problem is where you want to discover rules that describe large portions of your data

• such as people that buy X also tend to buy Y

Read more: Supervised and Unsupervised Machine Learning Algorithms

Supervised Learning

This is simple and you would have done it a number of times, for example:

1. Cortana or any speech automated system in your mobile phone trains your voice and then starts working based on this training.
2. Based on various features (past record of head-to-head, pitch, toss, player-vs-player) WASP predicts the winning % of both teams.
3. Train your handwriting to OCR system and once trained, it will be able to convert your hand-writing images into text (till some accuracy obviously)
4. Based on some prior knowledge (when its sunny, temperature is higher; when its cloudy, humidity is higher, etc.) weather apps predict the parameters for a given time.

5. Based on past information about spams, filtering out a new incoming email into Inbox (normal) or Junk folder (Spam)

6. Biometric attendance or ATM etc systems where you train the machine after couple of inputs (of your biometric identity - be it thumb or iris or ear-lobe, etc.), machine can validate your future input and identify you.

Unsupervised Learning

7. A friend invites you to his party where you meet totally strangers. Now you will classify them using unsupervised learning (no prior knowledge) and this classification can be on the basis of gender, age group, dressing, educational qualification or whatever way you would like. Why this learning is different from Supervised Learning? Since you didn't use any past/prior knowledge about people and classified them "on-the-go".

8. NASA discovers new heavenly bodies and finds them different from previously known astronomical objects - stars, planets, asteroids, blackholes etc. (i.e. it has no knowledge about these new bodies) and classifies them the way it would like to (distance from Milky way, intensity, gravitational force, red/blue shift or whatever)

9. Let's suppose you have never seen a Cricket match before and by chance watch a video on internet, now you can classify players on the basis of different criterion: Players wearing same sort of kits are in one class, Players of one style are in one class (batsmen, bowler, fielders), or on the basis of playing hand (RH vs LH) or whatever way you would observe [and classify] it.

10. We are conducting a survey of 500 questions about predicting the IQ level of students in a college. Since this questionnaire is too big, so after 100 students, administration decides to trim the questionnaire down to fewer questions and for it we use some statistical procedure like PCA to trim it down.

I hope these couple of examples explain the difference in detail.