Calculus for Data Science & AI, Master derivatives, gradients, backpropagation, and autodiff for machine learning with PyTorch.
Course Description
Calculus is the foundation of modern machine learning and AI—but most courses either stay too theoretical or skip the math entirely. This course bridges that gap.
Calculus for Data Science & AI is designed to help you truly understand how machine learning models learn, using calculus as a practical tool—not just abstract theory.
Instead of memorizing formulas, you’ll learn how calculus directly powers core concepts like loss functions, gradient descent, and neural networks.
We start by reframing machine learning models as mathematical functions and show how learning is simply the process of minimizing error. From there, you’ll build a strong intuition for derivatives, slopes, and sensitivity—then apply them step-by-step to real models.
As the course progresses, you’ll move into multivariable calculus, gradients, and Jacobians—key tools for understanding how modern AI systems operate under the hood.
You’ll then connect theory to practice by:
- Deriving backpropagation by hand
- Training a neural network from scratch using NumPy
- Understanding how gradients flow through deep networks
Finally, you’ll explore automatic differentiation, the engine behind modern ML frameworks, and see how tools like PyTorch handle gradient computation at scale.
By the end of this course, you won’t just use machine learning—you’ll understand how it works at a fundamental level.
This course is ideal for intermediate learners who want to go beyond high-level intuition and gain a deeper, more technical understanding of AI systems.
