Demystifying Machine Learning

Summary:

• Assume that there is a ‘true’ but unknown distribution.
• You have some examples that you assume are drawn from this ‘true’ distribution.
• You learn a model to estimate the answer so that it is as close as possible to the ‘true’ distribution.