From probability fundamentals and Bayes' theorem to distributions, MLE/MAP, confidence intervals, and hypothesis testing — a collection of practice problems grounded in real ML and data analysis scenarios.
Bayesian updates, distribution simulation, CLT verification, MLE/MAP implementation, confidence intervals, hypothesis testing, and a full A/B test pipeline — implementing probability & statistics chapters 1–4 in code.
A complete guide to confidence intervals, the t-distribution, hypothesis testing fundamentals (null/alternative hypotheses, p-values, rejection regions, statistical power), various t-tests, and A/B testing.
A concise guide to the core ideas behind ML estimation: populations vs. samples, the Law of Large Numbers, the Central Limit Theorem, Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP), and regularization.
A guide to descriptive statistics for distributions — expected value, variance, standard deviation, skewness, kurtosis — along with joint distributions, marginal distributions, conditional distributions, covariance, correlation, and the multivariate normal distribution.
A comprehensive overview of the probability and statistics essentials for machine learning — from basic probability and conditional probability to Bayes' theorem, and the binomial, normal, and chi-squared distributions.