Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
