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Supervised Learning and Discriminative Modelling

              Supervised Learning is a type of Machine Learning where we teach models using examples that have labels. It
              uses labelled datasets to train algorithms to predict outcomes and recognise patterns.

              These labels tell the model the correct answer for each example. Discriminative Modelling is a special kind of
              supervised learning that focuses on learning how to distinguish different classes. It looks at the features of the
              data to figure out which class it belongs to.


              Supervised Learning

              Supervised learning is a machine learning where a model is trained on a labelled dataset, implying that each
              input data point is associated with a corresponding output label. The goal of supervised learning is to learn the
              mapping between input data and output labels, enabling the model to make predictions on new, unseen data.
              Supervised learning is when we train the machine using labelled data. The machine is provided with a new set
              of labelled data so that the supervised learning algorithm analyses the training data and generates the most
              suitable and related outcome from the trained-labelled data. Labeled data contains data with the correct output
              or classification. In simple words, input data is paired with the desired output thus making the machine learn to
              predict the output for new input data.

              For example, in the given images, first is the input image and characteristics of this image are marked as boy
              and ball, which can be seen in center image. Now, according to supervised learning it has to learn the mapping
              between input labels and output labels, which is shown in last image and highlights "ball" as red, "boy" as purple
              and "boy playing with a ball" in a rectangle.


                                                                                                  Output
                             Input                    Features of given image
                                                                                             Label for the item
















                                                          Boy      Ball                    Boy playing with a ball

              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.


              Discriminative Modelling
              Discriminative modelling is an approach in machine learning where the focus is on learning the boundary or
              decision boundary that separates different classes or categories directly from the data. So, if an image contains
              a combination of Dogs and Cats, the model is able to tell which is a Dog and which is a Cat.





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