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In  supervised  learning,  the  algorithm  learns  from  labelled  data,  where  each  training  example  is  paired  with  a
                 corresponding target label. The goal is to learn a mapping from input variables to output labels. During training, the
                 algorithm adjusts its parameters to minimise the difference between predicted and actual labels.
                 Building, expanding, and successfully implementing accurate supervised machine learning models requires time and
                 technical expertise from a team of highly trained data scientists. In the real-world, supervised learning can be used for
                 risk assessment, image classification, fraud detection, spam filtering, etc. Take a look at the examples below:
                 Example 1:


                                                                                           It is a
                             INPUT RAW               Supervisor                           mango !
                                DATA        Training data set  Desired Output   OUTPUT







                                                  Algorithm      Processing    Model Trained


                                                       Model Training


                                                                                 INPUT
                 Step 1:   You provide the system with images of mangoes and tag them as mangoes. This type of input is referred to
                         as labelled data.

                 Step 2:   The model learns from the labelled data and next time you ask it to identify a mango, it can do it easily.
                 That’s exactly how supervised learning works.
                 Example 2:

                 Many voice assistants, including Apple's Siri and Amazon's Alexa, use supervised learning algorithms to process and
                 interpret spoken instructions. The algorithms are trained on a dataset of labelled speech data (transcribed speech and
                 text), which they then use to transcribe and interpret spoken commands.












                                         Analog Audio          Analog to Digital Audio   Pattern Recognition
                                                                    Conversion

                 Supervised Learning Algorithms and Their Use

                 Supervised learning involves two primary algorithms: regression and classification.
                 ●   Regression  algorithms  create  a  mapping  function  from  the  input  data,  allowing  us  to  predict  continuous
                    outcomes.  They  are  used  if  there  is  a  relationship  between  the  input  variables  and  the  output  variables.  For
                    instance, predicting house prices based on features like size and location is a task for regression.
                 ●   Classification algorithms, on the other hand, involve creating a function that assigns data points to specific categories. They
                    are used when the output variable is categorical, implying there are two classes such as Yes-No, Male-Female, True-False, etc.

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