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V i v a V oce Q u esti ons








             1.   What is an outlier?
             Ans. An outlier is a data point that stands out from the rest. They represent measurement inaccuracies and poor
                 data collection.
             2.   What is at the core of every AI project?
             Ans. Finding patterns in data.

             3.   Which machine learning algorithms do the following questions denote?
                 (i)   How much or how many?
                 (ii)  Can the data be grouped?
                 (iii)  Is there any unusual pattern in the data?
             Ans.  i)  egression     ii)  lustering     iii) Anomaly Detection

             4.   Name the five processes of Design Thinking Methodology.
             Ans.  mpathise, Define, Ideate,  rototype,  est
             5.   Which step of the DT methodology involves brainstorming?
             Ans. Ideate

             6.   What is Problem Decomposition?
             Ans. The  process of  breaking  down  a  major  problem into  smaller,  more  manageable  sub-problems is known
                 as decomposition.  arge issues are disproportionately more difficult to solve than small problems, hence,
                 decomposition is carried out.
             7.   List the steps of Problem Decomposition.
             Ans. (i)   Understand the problem and express the problem in your own words.
                 (ii)  Break down the problem into several big parts. Write them down on paper.
                 (iii)  Divide any larger complicated part into smaller pieces. Continue this until all parts are small.

                 (iv)  Code the smaller parts one by one.
             8.   Explain the step "Data Understanding".
             Ans. After the initial data collection, techniques such as descriptive statistics and visualisations can be applied to
                 datasets to evaluate the content, quality, and initial insights of the data. Additional data collection may be
                 required to fill the gap.
             9.   Why is feedback important in AI project cycle?
             Ans. Data scientists can utilise feedback to improve the model's accuracy and utility by analysing it. They can
                 automate any or all of the feedback gathering, model assessment, refining, and redeployment phases to
                 speed up the model refresh process and improve results.
             10.  Which splitting technique is sufficient for larger datasets?

             Ans. Train Test Split







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