Statistics
Linear, logistic and generalized linear models.
An end-to-end regression case study: price modeling, least squares, multivariable regression, validation, interpretation, model persistence and an interactive pricing desk.
Learn logistic regression through a bank-fraud problem: probability, odds, log-odds, sigmoid curves, classification thresholds, precision, recall, F1 and business decisions.
Derive the ordinary least squares estimator, understand what it assumes, and implement it in a few lines of NumPy.