Page 173 - Robotics and AI class 10
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Data Visualisation Technique 7

             Name of the Representation                                 Bubble Map
             Description                    It is a combination of a bubble chart, data visualisation and a map. It is used
                                            to visualise location and proportion using circles over geographical regions
                                            with the area of the circle being proportional to its value in the dataset.
             How to draw?




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             Suitable for which type of data? It is good for comparing proportions over geographic regions without the
                                            issues caused by regional area size. For example, showing the earthquake
                                            prone areas in a country, geographical distribution of whales in the northern
                                            hemisphere, number of tigers in the national parks of India.


                    What is Modelling?


            Modelling or data modelling is defined as the process of designing decision-making algorithms that has to be
            trained on a set of data (which was acquired at the data acquisition stage for the problem you scoped in the
            problem-scoping stage) and apply that learning to recognise certain types of patterns.
            Once the data is visualised and trends are formed, we need to work with algorithms to prepare the AI model.
            This can be done by designing our own models or using the existing AI models. Before we go into the details of
            modelling, let us first understand the following important terms:
               • Artificial Intelligence: AI refers to any technique that enables computers to mimic or imitate, develop and
              demonstrate human intelligence. They are machines that can perform tasks that they are programmed for.
              AI enables machines to think without any human intervention.
               • Machine Learning: Machines need to learn the ways of humans by learning the techniques and processes. So
              machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to improve
              with experiences. So machines learn from their mistakes and take them into consideration in the next iteration, this
              way they keep improving with experience. For example, Snapchat filters and
              Netflix recommendations.                                                           Artificial
                                                                                               Intelligence
               • Deep  Learning: Machines can draw meaningful inferences from large              Machine
              volumes of datasets. In deep learning, the machine is trained with a huge          Learning
              amount of data which helps it train itself. Deep learning is a machine learning
              algorithm that is inspired by the functionality of our brain cells called neurons.   Deep
              For example, Google translate, image recognition in social media apps.              Learning



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