Machine learning!!! So remember there are multiple ways to execute machine learning problems. Here Let's we discuss about few of those........... Supervised learning : The computer is presented with example inputs and their desired outputs, given by a “mentor”, and the goal is to learn a general rule that maps inputs to outputs. The training process continues until the model achieves the desired level of accuracy on the training data. few of the examples are: Image Classification: You train with images/labels. Then in the future you give a new image expecting that the computer will recognize the new object. Market Prediction/Regression: You train the computer with historical market data and ask the computer to predict the new price in the future. Unsupervised learning : No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It is used for clustering population in different groups. Unsupervised learning can be a goal in itself...
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