Calculus plays a crucial role in machine learning, underpinning the optimization techniques that drive algorithm performance. This page explores how differential and integral calculus are used to develop and refine machine learning models. Learn about gradient descent, cost function minimization, and other calculus-based methods that are essential for training algorithms effectively. Understanding these concepts provides deeper insights into the mechanics of machine learning, enabling the development of more accurate and efficient models.