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 hands-on coding practice for implementing matrix operations, Gaussian elimination, eigendecomposition, and PCA from scratch using NumPy. Each problem includes step-by-step hints and complete solution code.
Hands-on coding assignments implementing numerical differentiation, gradient descent, perceptron backpropagation, and Newton's method from scratch with NumPy. Each problem includes step-by-step hints and complete solution code.
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.