Unsupervised learning is a type of machine learning where we do not have labelled data and we don't know how the machine came to design the algorithm that can help it cluster the data into different distinct groups.

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Reinforcement learning is a type of machine learning where we have one or more agents that take actions in an environment to accumulate the reward that they receive ultimately to the maximum reward possible.

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The machine learning recipe includes data that must be either labelled or unlabelled, a model which much be trained on that data, and a compute component or unit that must be capable of processing power to train the model.

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In a regression model, the dependant variable is the one you are trying to predict from one or more inputs, which are your independant variables.

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