Calculus for Machine Learning and Data Science

After completing this course, learners will be able to: Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton's method) iterative methods Visually interpret differentiation of different types of functions commonly used in machine learning Perform gradient descent in neural networks with different activation and cost functions Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you'll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics.

Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you'll understand the mathematics behind all the most common algorithms and data analysis techniques plus the know-how to incorporate them into your machine learning career.