Page 174 - Robotics and AI class 10
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Artificial intelligence is an umbrella term that holds machine learning and deep learning. Deep learning follows a
        specific learning approach which is a subset of machine learning comprising multiple machine learning algorithms.
        An important  stage in the process of AI project cycle where we decide on the technique  to be followed for
        building a model from the prepared data. It is a mathematical approach in which an algorithm is designed as
        per the requirement of the system which is ready to be installed to analyse the data technically. In the previous
        stage of data exploration, we used the graphical representation of data to make it easy to understand the trends
        and patterns. But when it comes to machines, it only understands the language of 1s and 0s so they only rely on
        mathematical representation of data.
                                                                                    #Digital Literacy
                    Video Session


               Scan the QR code or visit the following link to watch the video:
               All Machine Learning Models Explained in 5 Minutes | Types of ML Models Basics
               https://www.youtube.com/watch?v=yN7ypxC7838&t=126s






        Prediction
        As the word suggests, prediction in AI refers to the outcome/output of an AI algorithm after it has been trained on
        a historical dataset, used by the model on the new dataset while forecasting the likelihood of a particular event/
        outcome. For example, the weather forecast is based on the current weather trend data, and the AI model uses
        historical dataset over the new dataset available and predicts the weather expected in the near future.


        Approaches in AI Modelling
        An important stage in the process of AI project cycle where
        we decide on the technique to be followed for building a                     AI Models
        model from the prepared data. It is a mathematical approach
        in which an algorithm is designed as per the requirement of
        the system which is ready to be installed to analyse the data   Rule Based                Learning Based
        technically. In the previous stage of data  exploration,  we   Approach                     Approach
        used the graphical representation of data to make it easy to
        understand the trends and patterns. But when it comes to machines, it only understands the language of 1s and
        0s so they only rely on mathematical representation of data. AI modelling techniques can be broadly classified into
        the following two approaches:


        Rule Based Approach
        This approach is based on a set of rules and facts defined by the developer and fed to the machine to perform its
        task accordingly to generate the desired output. These models can operate with simple basic information and data.

        To explain it further, let's take an example. If you have a dataset that consists of weather conditions, a basis which
        we can predict if the lion would be visible on a specific Safari Day to the tourists. The parameters can be cloud
        cover, temperature, wind speed, humidity. When these parameters are recorded and fed in the machine giving
        the favourable combinations when the Lion would be visible and rest can be considered that the lion would not
        be visible. Now, to test the model, the machine is given a scenario of the cloud cover, temperature, wind speed,
        humidity. The model will compare the same with the fed in the dataset and if there is a match, would let know if
        the lion would be visible or not. This is called a rule-based approach.

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