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.
Test your understanding across all seven chapters — from descriptive statistics to unsupervised learning — with concept, calculation, code, and scenario problems.
A hands-on overview of unsupervised learning essentials — PCA, K-Means, hierarchical clustering, Gaussian Mixture Models, and feature scaling — all with code examples.
A hands-on walkthrough of KNN, decision trees, random forests, AdaBoost, and gradient boosting — the core concepts behind tree-based ensemble models, complete with code examples.
A hands-on guide to classification algorithms — covering Naive Bayes, discriminant analysis, logistic regression, confusion matrices, ROC/AUC, and techniques for handling imbalanced data, all with code examples.
A code-driven walkthrough of regression essentials: simple and multiple linear regression, residual diagnostics, categorical variable encoding, and polynomial/spline regression.
A comprehensive guide to statistical experiments — covering A/B testing, hypothesis testing, p-values, t-tests, ANOVA, chi-square, and multi-armed bandits, with code examples throughout.
A code-driven walkthrough of the core concepts in sampling distributions: random sampling and bias, bootstrap, confidence intervals, the normal distribution, t-distribution, and binomial and Poisson distributions.
A practical overview of data types (continuous, discrete, categorical, etc.), location estimation (mean, median), and variability estimation (variance, standard deviation), with code examples throughout.