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Course Description

The most basic supervised learning algorithm is linear regression. Topics include single and multiple regression, and how to evaluate and validate these methods. Learn about best practices when building a model, how to split a dataset into training, how to test sets and metrics to evaluate errors in regression, and the concepts of time series regression.

Prerequisites

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