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Learn the fundamentals of linear regression in this lecture covering prediction functions that map input variables to output variables, understanding sum of squared errors and root mean square error (RMSE) as evaluation metrics, and exploring the algorithmic approach for solving linear regression problems in one-dimensional scenarios.
