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Steps in Machine Learning
        The Steps in Machine Learning are broadly categorised as follows:
           • Data Collection: As explained earlier, the data collection is the base in the process of Machine Learning. The
           reliability and the quality of data needs to be assured for your model to predict accurately.
           • Data Preparation and Wrangling: The data collected needs to be prepared and made in a structured manner
           so that the correlation between the variables and classes can be understood. The process would require data
           randomization and cleaning. The cleaning would involve removal of irrelevant data, duplicate data, missing
           values, followed by restructuring of data by adjustment of rows and columns, or their index numbers. Once the
           data is cleaned and converted to a usable format, the data need to be split, one set to be used as Training Data
           and the other as Testing Data.
           • Model Selection: The Model Selection or Model Building is determined based on the outcome you want to
           achieve. It is a build using various analytical techniques of machine learning which are best suited for the task
           at hand, whether it is do with speech recognition, image recognition, numerical data, text data, prediction, etc.
           • Training the Model: In this process, we use the data prepared for Training and allow the Model Algorithm to
           process it and understand the patterns, features and rules, to be able to predict. The further training helps the
           model to predict more accurately over the period of time and get closer to completing the task it is designed to do.
           • Testing, Evaluating and Tuning the Model: The Testing data is used to check the accuracy of the Models
           prediction. The evaluation of the results and further, Tuning of the algorithm helps the model to achieve
           complete accuracy in predicting.
           • Deployment and Prediction: Once the model is tested, it is deployed in the real world. The unseen real
           world data is fed to the model, which it should be able to use and predict with great accuracy as it has been
           thoroughly exposed to the testing data.
                                                           Data Collection


                                                   Data Preparation and Wrangling
                                    Machine Learning Process  Training the Model

                                                         Model Selection







                                             Testing, Evaluating and Tuning the Model



                                                    Deployment and Prediction



                                                                                    #Digital Literacy
                    Video Session

               Scan the QR code or visit the following link to watch the video:

               The role of data in Artificial Intelligence
               https://www.youtube.com/watch?v=oyhdkoPYRVs






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