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                    4.  Why is clustering unsupervised?
                   Ans.  Clustering is an unsupervised machine learning technique that automatically divides the data into clusters or groups
                       of similar elements. The algorithm does this without any knowledge of how the groups should look in advance. So,
                       clustering is rather used for the discovery of knowledge rather than for prediction. It provides an idea of natural
                       groupings that are within data.
                       Without advanced knowledge of what a cluster includes, how can a computer know where a group begins or ends? The
                       answer is simple. Clustering is driven by the principle that objects within a group should be very similar to each other,
                       but very different from the objects outside. The similarity function can vary across different applications, but the basic
                       idea is always the same—group the data so that the related elements are placed together.

                    5.  Why is KNN algorithm required? Explain with the help of example.
                   Ans.  The K-Nearest Neighbor (KNN) method determines the colour of the ball based on proximity of new ball or datapoint.
                       If the new ball is close to the red colour group, it is labelled as red; if it close to the blue colour group, it is labelled as
                       blue; and if it close to the green colour group, it is labelled as green.
                 C.   Competency-based/Application-based questions:                   #Problem Solving & Logical Reasoning

                    1.  During a class discussion, Sumit, the geek of the school, enquired that how we can differentiate between Machine
                       Learning and Artificial Intelligence. He was very well aware of the definitions but he was looking for concrete examples
                       that actually depicted the connection and distinguish between the two. Would you help Ms. Geeta, the computer
                       science teacher, present in the class to come up with the convincing examples for the same.

                   Ans.  Machine learning (ML) is a subset of Artificial Intelligence (AI) that involves the development of algorithms that allow
                       computers to learn from and make decisions based on the visual data. Following examples illustrates the same:

                       In AI, we conceptualise techniques to perform visual perception, speech recognition and decision making.
                       When we enter the sphere of machine learning, let’s say we target on classification of images we have to develop
                       classification software using algorithm like (Convolutional Neural Networks) CNN algorithm. This example shows ML
                       as employer of a specific method used within the broader AI goal of enabling computers to understand and process
                       visual information

                       Another  Example  is  when  AI  aims  to  understand  the  nuances  of  human  speech,  we  work  upon  algorithms  like
                       RNN (Recurrent Neural Networks), this part of working upon an algorithm is ML where as the goal of achieving the
                       understanding of human language and every step proceeding towards it comes under AI.

                       Assertion and Reasoning questions:
                         Direction: Questions 2-4 below, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions
                       selecting the appropriate option given below:
                       a.  Both A and R are true and R is the correct explanation of A.
                       b.  Both A and R are true but R is not the correct explanation of A.
                       c.  A is true but R is false.
                       d.  A is false but R is true.
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