Calculus for Artificial Intelligence focuses on understanding how AI models learn and improve. It covers limits, derivatives, gradients, and optimization techniques that are essential for training models. The course explains how calculus is used in loss functions, gradient descent, and neural networks, providing the mathematical foundation behind learning, prediction, and decision-making in AI systems.

Linear Algebra for Machine Learning introduces the core mathematical concepts behind ML algorithms. It explains how vectors and matrices represent data, how transformations work, and why ideas like eigenvalues and matrix decompositions are important for tasks such as dimensionality reduction. The course connects theory with practical machine learning applications, building a strong foundation for understanding and improving models.