Subjects

Logistic Regression & Linear Classifiers

Master logistic regression: sigmoid and log-odds, odds-ratio interpretation with worked numbers, maximum likelihood and log-loss, linear decision boundaries, cost-aware thresholds, calibration, class imbalance handling, multiclass softmax, and how it compares to SVMs, naive Bayes and trees.

Hypothesis Testing & Statistical Inference

Learn hypothesis testing from first principles: p-values and their misreadings, Type I/II errors and power, z, t, Welch, paired, chi-square and proportion tests, non-parametric alternatives, confidence intervals, effect sizes, multiple-comparison corrections, bootstrap and sample-size formulas.

Linear Regression

Learn how ordinary least squares fits a line, how to read coefficients (dummies, interactions, log transforms), the Gauss-Markov assumptions and how to diagnose violations, R-squared traps, standard errors and confidence intervals, VIF, leverage and Cook's distance.

A/B Testing & Online Experimentation

Learn to design trustworthy online experiments: pick metrics and randomisation units, size a test with power and MDE, catch SRM and peeking, cut variance with CUPED, and read lifts, segments and holdouts correctly.