This course builds on the foundations introduced in Level 1 and takes you deeper into the mathematics behind modern machine learning and deep learning algorithms. You will develop an intuitive understanding of gradients, Hessian matrices, Taylor series, constrained optimization, and advanced optimization algorithms such as Momentum, RMSProp, and Adam. Rather than memorizing formulas, you will learn how these mathematical tools explain the behavior of machine learning models during training.
Throughout the course, every concept is connected to practical machine learning applications through visual explanations and geometric intuition. You will also explore multivariable integrals and probability as essential components of modern AI systems.
This course is designed for learners who already understand basic derivatives and want to confidently study optimization and deep learning mathematics. Downloadable PDF notes accompany every lecture to support your learning and future review. By the end of the course, you will have a strong conceptual framework for understanding how machine learning models learn, optimize, and converge.
