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For example, classifying emails as spam or not spam uses a classification function to determine the appropriate
                  category for each email. Following are the uses of Supervised Learning:

                                      Estimating Life
                                       Expectancy  Population Growth                Identity Fraud     Image
                                                     Prediction                      Detection      Classification
                             Market
                           Forecasting

                           Weather
                          Forecasting                                Supervised
                                            Regression                Learning               Classification


                           Advertising
                            Popularity
                           prediction
                                                                                      Customer       Diagnostics
                                                                                      Retention
              Advantages of supervised learning:
                 •  With the help of supervised learning, the model can predict the output on the basis of prior experiences.

                 •  In supervised learning, we can have an exact idea about the classes of objects.
                 •  Supervised learning model helps us to solve various real-world problems such as fraud detection, spam filtering,
                etc.

              Disadvantages of supervised learning:
                 •  Supervised learning models are not suitable for handling the complex tasks.
                 •  Supervised learning cannot predict the correct output if the testing data is different from the training dataset.
                 •  Training requires a lot of computational time.
                 •  In supervised learning, we need enough knowledge about the classes of object.


              Unsupervised Learning
              As the name suggests, unsupervised learning is a machine learning technique in which models are not supervised
              using training dataset. The machine learns through observation and finds patterns in data. The system will explore
              the data and draw inferences from the data set to describe the hidden patterns in the unlabelled data. Unsupervised
              machine learning algorithms are used when the information used to train is neither classified nor labelled.
              For example, if somebody gives you a basket full of different fruits and asks you to separate them, you will probably
              do it based on their colour, shape, and size, right? Unsupervised learning works in the same way. For example,


                         Raw Data is input                                                    Output

                                       • Unknown Input     Algorithm
                                       • No Training Data Set





                                            Interpretation             Processing


                                                        Model Training                    Model is trained



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