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What is the difference between supervised and unsupervised learning with reference to machine learning and AI? Please provide basic explanation with an example.

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They can be differentiated based on the following category-

1. Aim

Supervised learning: the aim is to approximate the mapping function such that whenever there is a new input data then the corresponding output variable can be predicted.

Unsupervised Learning: aim of is to model the distribution in the data in order to learn more about the data.

 2. Input Data

 Supervised learning:Uses Known and Labeled Data as input

 Unsupervised Learning:Uses unknown and Labeled Data as input

3. Computational Complexity

Supervised learning:Very Complex

 Unsupervised Learning:Less Complex

 4. Real Time

Supervised learning:Uses off-line analysis

 Unsupervised Learning:Uses Real Time Analysis of Data

 5. Accuracy of results

Supervised learning: Accurate and Reliable Results

Unsupervised Learning: Moderate Accurate and Reliable Results

If you want to learn Types of Supervised and Unsupervised Learning refer to this.

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Supervised Learning

 
  • It involves input variables and output variables and an algorithm to map the input variable to the output variable

  • The main aim of supervised learning is that it has to provide good mapping function so that whenever there is any new input value then we can easily predict the correct output value.

  • For example, suppose there is a basket filled with some fruits the task is to arrange the same type of fruits in one place. And it is given that the fruits are apples, bananas, cherries, grapes. Suppose one knows from their previous work about the shape of each and every fruit present in the basket. So, it is easy for them to arrange the same type of fruits in one place. Here the previous work is known as the training data.

 
  • Accurate and Reliable Results

Unsupervised Learning

 
  • It involves only the input data and the corresponding output value is not known to us.

  • The main aim of unsupervised learning is that it models the distribution of data for discovering more about that data. It can be done through deep learning.

 
  • For example, suppose there is also a basket filled with some fruits the task is to arrange the same type of fruits in one place. And this time information about the fruits is not given. So how to group similar fruits without any prior knowledge about those. First, we will see the basic factors like color, shape, size. And according to that, it will discover the data.

  • Moderate Accurate and Reliable Results

 

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