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.
A collection of concept problems applying linear algebra — matrix singularity, rank, eigenvalues, PCA, and more — to real machine learning scenarios. Each problem is grounded in situations you'll commonly encounter in actual ML pipelines.
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.
Wrap up the core of linear algebra: geometric interpretation of determinants, basis and span, eigenvalue/eigenvector computation, how PCA works under the hood with full derivations, and Markov matrices.
A comprehensive guide to vector norms, dot products, scalar multiplication, the concept of linear transformations, matrix multiplication, inverse matrix computation, and how all these ideas connect in neural networks (the perceptron).
A clear walkthrough of solving linear systems via elimination, computing Row Echelon Form (REF) and Reduced Row Echelon Form (RREF) through Gaussian elimination, and using Rank to determine the number of solutions.
A walkthrough of how systems of linear equations are used in machine learning — covering singular vs. non-singular matrices, linear independence/dependence, and determinant calculation.
A collection of concept exercises applying differentiation, loss function optimization, gradient descent, and backpropagation to real-world machine learning scenarios. Each problem is grounded in situations you commonly encounter when training models in practice.
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 deep dive into the principles of gradient descent and learning rate, perceptron regression and classification, backpropagation, and Newton's method with the Hessian — the core concepts of neural network optimization.
A deep dive into the core of machine learning optimization: minimizing loss functions, differentiating squared and log loss, understanding partial derivatives and gradients, and using the gradient to find minima.
Build an intuitive understanding of derivatives — the engine behind ML optimization — and master the differentiation rules for constants, polynomials, exponentials, logarithms, and trig functions, plus scalar multiplication, sum, product, and chain rules.