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What are Labels?                                                                       Labels

                 The process of assigning a meaningful name to a category of data (features)
                 is  known  as  data  labelling.  It  can  easily  be  identified  as  column  heading.   Car Type  Colour Price
                 These labels help to clarify what the data represents. For example, 'Car Type'
                                                                                           Sedan        Silver   12 Lac
                 indicates the category to which a car belongs, such as Sedan, MUV, or SUV.
                                                                                           MUV          Black    17 Lac
                 Labelled Data                                                             SUV          Red      30 Lac


                 Marked or tagged data, which easily identifiable is called labelled data. For example, name, type, colour, etc.
                 Unlabelled Data


                 Data that is not marked/tagged is called unlabelled data. It is also known as the raw form of data.
                                               Labelled Data         Unlabelled Data




                                            Apple  Orange  Banana
                 What do you mean by a Training Dataset?

                 A collection of data provided to a machine learning model to help it analyse and learn patterns is called training
                 data. Just like how a teacher explains any concept to students through examples and illustrations, helping them
                 understand and solve similar problems. Similarly, a set of labelled data (features and their corresponding labels) is
                 used to train the AI model, teaching it how to make accurate predictions or decisions.
                 What do you mean by a Testing Dataset?

                 The testing data set is a collection of data provided to a machine learning model to evaluate how well it has
                 learned to make predictions. Just like how a teacher gives a test to students after teaching a concept to assess their
                 understanding and identify any gaps. Similarly, a set of labelled data that the model has not seen before is used to
                 test its performance, ensuring it can make accurate predictions or decisions on new, unseen data.

                         Modelling



                 AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs.
                 That is, writing codes to make a machine artificially intelligent. The model is trained using data.
                 There are mainly two types of AI models:
                                                                            AI Model




                                            Learning-based                                            Rule-based



                                    Machine Learning                                   Deep Learning




                       Supervised     Unsupervised   Reinforcement           Artificial Neural        Convolutional
                        Learning        Learning        Learning                Network              Neural Network


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