In nontechnical terms, machine learning models work by using large datasets of training data to reinforce 'good' responses, and to punish 'bad' responses. By using human feedback, creating these datasets and knowing whether the data is good or bad can be crowdsourced. 
In more technical terms, a machine learning model uses mathematical tools, such as regression, to create a model that describes the data. By supplying parameters (data), the model can output a response. Based on the response compared to the labels ('true' data), the model is updated to better describe it.
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