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Consider an example of the items purchased by customers:




                        Consider a supermarket example wherein
                           • Customer A buys bread, butter, and milk
                            • Customer B buys rice, bread, and butter


                                                                             Customer A            Customer B
              Based on the purchase pattern of customers A and B, can you predict any Customer X who buys bread will most
              probably buy?











                                      Customer A            Customer B            Customer X

              In this case, we might discover an association rule such as:
              "If customer A buys 'Milk', they are likely to buy 'Cereal' or 'Bread'."
              Therefore, such meaningful associations can be useful to recommend items to customers. This is called Association rule.

                            Reboot


                  1.  Which learning approach uses labelled data for training?
                      a. Supervised Learning          b.  Reinforcement Learning    c. Unsupervised Learning

                  2.  The target variables is categorical in ________________________ problem.
                      a. Regression                   b. Clustering                 c. Classification
                  3.  Which algorithmic model would you use when you have to predict a continuous valued output?
                      a. Regression                   b. Clustering                 c. Classification

                  4.  Which of the following is false about Reinforcement Learning?
                      a. Uses Reward Mechanism             b. Target is to Maximise the Reward
                      c. Predicts a continuous value as output
                  5.  Clustering is _____________ learning and its goal is to ______________.

                      a. Supervised, Classify data points, into different classes
                      b. Unsupervised, Divide the data, points into different groups
                      c. Unsupervised, Predict the output, based on input data points



              Sub-Categories of Deep Learning

              Deep Learning helps software learn to do tasks by using a lot of data. The machine is given large amounts of
              information, which helps it learn and improve on its own. These smart systems can even create their own rules.
              There  are  two  types  of  Deep  Learning  Models:  Artificial  Neural  Networks  (ANN)  and  Convolutional  Neural
              Network (CNN).

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