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Data Modelling Techniques


            In AI modelling, we will be developing different algorithms which we call models, and these models can be
            trained  to  get  intelligent  output.  In  other  words,  we  can  say  we  write  codes  to  make  a  machine  artificially
            intelligent.
            AI modelling techniques can be broadly classified into two approaches. Let us learn about them in detail.


            Rule-Based Approach

            Rule-based approach is based on a set of rules and set of facts already fed to the machine to generate the
            desired output.  These models can operate with simple basic information and data. The relationship or patterns
            in the data is defined by the developer.

            To explain it further, let's take an example. You have a dataset comprising 100 images of cars and 100 images
            of cycles. To train your machine, you feed this data and label each image as either a car or a cycle. Now if you
            test the machine with an image of a car, it will compare with the trained data and according to the labels of the
            trained data it will identify it as a car. This is called a rule-based approach. The rules given to the machine in this
            example are the labels assigned to the training data.




                                                                                      Rule-based


                                                             Labelled Datasets



                                                           Used to Train Machine        Model

















                                                                 Output               Used for Testing  Training Data




                             Machine Identifies the Image as Car                        Testing Data


            Learning-Based Approach

            Learning-based approach refers to the model where the relationship or patterns in the data are not defined by
            the developer. Random data is fed into the machine and the machine develops its own pattern or trends based
            on data outputs. It is an alternative method to address some of the challenges of rule-based systems.


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