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• Observations: It is a method of collecting data by watching facts as they occur. Using the observation technique
           data can be analysed and used for testing the model.
           • Application  Programming Interface (APIs): APIs are a  set of  functions and  procedures that  allow  one
           application to connect to another. So one of the ways of collecting data is through APIs that can be used to
           collect data from social media services for analysis.

        There are times using the internet, we acquire unauthentic data from websites for our AI project. Extracting
        private data can be an offense. So keeping this in mind we should ensure the data is collected from open sourced
        websites hosted by the government. They are one of the most reliable and authentic sources of information.
        These portals have information collected in suitable format that can be downloaded. Some of the open sourced
        government portals are data.gov.in, india.gov.in, etc.


        Importance of Programming and Algorithms in Teaching Machines
        Teaching Machines/Computers on how to make decisions is not the same as teaching humans. There is a huge
        amount of data that needs to be processed to reach the relevant outcome, so subjective decision making can
        happen. How the model would process the relevant data, the instructions to the machine are given in the form
        of Algorithms/Programs which are understood by machines and accordingly processed. These are also known as
        ADS (Algorithmic Decision Systems). However, programming and algorithms can be used to create models that
        can learn to make subjective decisions.

        Here are some of the ways that programming and algorithms can be used to teach machines subjective decision
        making:
           • Machine learning: Machine learning can be used to train machines to learn from data. By feeding a machine
           learning model with a large amount of data, the model can learn to identify patterns and make predictions. This
           can be used to teach machines to make subjective decisions by feeding the model with data that includes both
           the objective facts of a situation and the subjective feelings of the people involved.
           • Natural language processing: Natural language processing can be used to teach machines to understand
           the nuances of human language. This includes the ability to understand the emotional content of words and
           phrases. By understanding human language, machines can be taught to make subjective decisions by taking
           into account the subjective feelings of the people involved in a situation.

           • Programming: Programming can be used to create algorithms that define the rules that machines will use to
           make decisions. These rules can be based on the objective facts of a situation, the subjective feelings of the
           people involved, or a combination of both.
        By using programming and algorithms, machines can be taught to make subjective decisions that are more
        informed, creative, and adaptable. This is beneficial in a variety of fields, such as art, design, business, customer
        service, and healthcare.


        Example such as fruit sorting
        For example, suppose you have a dataset of 1000 images of fruits from your garden. Now you do not have
        any clue as to what trend is being followed in this dataset as you don't know their names, colour or any other
        feature. Thus, you would put this into a learning approach based AI machine and the machine would come up
        with various patterns it has observed in the features of these 1000 images. It might cluster the data on the basis
        of colour, size, shape, etc. It might also come up with some very unusual clustering algorithm which you might
        not have even thought of!


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