Explore the fundamental concepts of cost function and gradient descent in machine learning and neural networks in this 32-minute video lecture. Dive into the mathematical foundations that drive the optimization process in ML algorithms. Learn how cost functions measure the performance of models and how gradient descent iteratively improves model parameters. Gain insights into the practical application of these techniques in training neural networks and other machine learning models. Access additional resources and course materials to further enhance your understanding of machine learning and deep learning foundations.