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It takes advantage of historic knowledge as input to predict new output values. Equipment Mastering is made up of equally supervised learning (wherever the predicted output to the input is known owing to labeled info sets) and unsupervised Understanding (exactly where the expected outputs are not known resulting from the use of unlabeled knowledge