Help readers understand the mathematical interpretation of learning algorithms
Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc
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