Cost Function We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average) of all the results of the hypothesis with inputs from x’s and the actual output y’s. J(theta_0, theta_1) = dfrac {1}{2m} displaystyle sum _{i=1}^m left ( hat{y}_{i}- […]

Unsupervised Learning Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables. We can derive this structure by clustering the data based on relationships among the variables in the data. With […]

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