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CATEGORIES OF MACHINE LEARNING BASED MODELS

        On the basis of nature of input, machine learning based model can further be categorised into three types:
         u Supervised Learning                u Unsupervised Learning             u Reinforcement Learning


                                                       Machine Learning








                                   Supervised            Unsupervised          Reinforcement


        Supervised Learning

        Supervised Learning is a category of machine learning that uses      Letter grade            Percentage
        labelled datasets to train algorithms to recognise patterns in the
        input data and predict outcomes. Datasets play an important               A                 80 – 1100
        role in AI models. A model is said to be supervised if it learns
        from a labelled dataset. A label is basically used to categorise the      B                   65 – 79
        various items in the dataset into distinct groups. In Supervised
        learning, a human  expert creates the training  dataset by                C                   55 – 64
        assigning labels to the collected data. Thus, the leaning is called       D                   50 – 54
        Supervised.
        Let us understand the concept of labelling with the help of some          E                   0 – 49
        simple examples. In your class, you have seen that teachers use
        a grading system for the marks/percentage secured in examinations. These grades are labels that categorise the
        students according to their marks/percentage. The model accepts the marks of a students as input and predicts
        the grade based on the training dataset.
        In the banking sector, loan approval system is a great example of supervised learning where banks can train a
        model using past loan applicants' data including various data items such as income, credit score, and proposed
        loan amount, along with labels whether the loan was approved or rejected. On the basis of the data, the model
        can assess new applicants to classify whether the application is to be approved or rejected.


















        Let us learn the concept of supervised learning in chatbots. The general steps used to build chatbots are:
        Step 1:   Identify the specific tasks the chatbot will handle.

        Step 2:   Gather conversational data relevant to the defined purpose. The data should ideally be in a structured
                 format, with user queries and appropriate responses clearly mapped out.

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