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.
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 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.
Test your understanding across all seven chapters — from descriptive statistics to unsupervised learning — with concept, calculation, code, and scenario problems.