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Supervised Learning
                 In a supervised learning model, a labelled dataset is given to the machine. A labelled dataset is the information
                 which is tagged with identifiers of data. For example, clothes in a store are marked under various categories of
                 clothing like Shirts, Trousers, Coats, etc. They are further labelled as per gender and size. The Supervised Learning
                 is further categorized as:
                    • Regression: It is an example of rule-based AI model. In regression, the algorithm generates a mapping function
                   from the given data, as shown by the solid line in the given graph. The green dots shown in the graph are
                   the data values and the solid line here represents the mapping done for them. With the help of this mapping
                   function, we can predict the future data. For example, if we want to predict the temperature of a day in a year,
                   we can use past year’s temperature for that day as training data and can predict it for the coming year.



















                    • Regression  is  a  mathematical  approach  to  find  a  relationship  between  two  or  more  variables.  It  works  with
                   continuous data. This can be used for weather forecasting, time series modelling, etc. In order to get the best fit
                   results, the distance between the line and data points should be minimum.
                    • Classification: This is another rule-based AI model. It is a systematic grouping of observations in categories,
                   something like categorising plants, animals in different taxonomies by biologists. In classification you teach the
                   machine to perform with labelled data. Testing data is then classified as one of the labels of the training dataset.
                   The algorithm is able to determine which set a given data point belongs to by means of a classification function
                   represented by the dotted line. The model classifies datasets according to the rules given to it.























                 For example, if we want to train a model to identify if an image is of an onion or a ginger, we need to train it with
                 multiple images of both onion and ginger along with their labels. The machine will then classify images on the
                 basis of the labels and predict the correct label for testing data. Classification works on discrete dataset.




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