Dive into a 43-minute video tutorial on calculus exercises for machine learning, led by Weights & Biases experts Charles Frye and Scott Condron. Explore key concepts like little-o notation, gradients as linear approximations, and gradient descent through hands-on Python exercises using SymPy. Learn to implement and understand crucial mathematical foundations for ML, including checking little-o conditions, creating linear approximations, and applying gradients in optimization. Follow along with provided GitHub resources and complementary materials to deepen your understanding of calculus in the context of machine learning.