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At a Glance


              •  A capstone pro ect is a comprehensive, independent, and final pro ect undertaken as a part of a curriculum
               designed to assess the skills, kno ledge, and e pertise a student has acquired.
              •  A  successful  problem defining  process  requires  a  basic  analysis  and  evaluation  of  the  pro ect related
               problems, their reasons, and methods.
              •  Design  hinking methodology provides a solution based approach to solving problems.
              •  During coding,  e follo  problem decomposition methodology that can be applied to real life problems as
                ell.
              •  Once the business problem is clearly stated, the data scientist can define an analytical approach to solving
               the problem.

              •   he analytical approach chosen characterises the requirements for the data.
              •  During the initial data collection phase, data scientists identify available data sources  structured, unstructured,
               and semi structured) relevant to the problem area.
              •   he modelling stage, that begins  ith the initial version of the prepared data set, focuses on constructing
               predictive or descriptive models based on the previously stated analytic approach.
              •   he data scientist revie s the model during development and before deployment to determine its quality
               and ensures that it correctly and completely ans ers the business problem.
              •   he train test procedure measures the performance of machine learning algorithms  hen they need to make
               predictions on data that  ere not used to train the model.
              •   he training dataset is used to fine tune the machine learning model and train the algorithm.
              •   est dataset algorithms make predictions using the input elements from the training data.
              •   ross validation is a resampling technique for evaluating machine learning models on a small sample of
               data.
              •  A loss function determines ho   ell a certain algorithm models the data.

              •   oss  function  learns  to  lo er  prediction  error  over  time   ith  the  help  of  some   optimisation ob ective
               function.
              •   oss functions can be divided into t o categories  regression losses and classification losses.
              •      is sensitive to outliers.
              •   he  oot  ean  quare  rror      ) is a metric for determining ho   ell a regression line fits the data points.
              •   yperparameters are parameters  hose values govern the learning process.




                                                     Exercise




                                                 Solved Questions


                                          SECTION A (Objective Type Questions)
                  ui

        A.   Tick ( ) the correct option.
               .   hich of the follo ing is not a part of Design  hinking  rocess
                  a.   mpathise                                  b.    ympathise
                  c.   rototype                                  d.   Define

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