Mathematics for Machine Learning: Multivariate Calculus

This course provides a basic understanding of multivariate calculus, which is essential to construct several standard machine learning approaches. This is one of the best online calculus courses. The course begins with a refresher on the "rise over run" formulation of a slope, which is then converted to the formal definition of a function's gradient. The course then moves on to developing a collection of tools for making calculus simpler and faster. Instructors then learn how to construct vectors that point uphill on multidimensional surfaces and put their newfound knowledge to use in an interactive game. Instructors examine how they might utilize calculus to construct approximations to functions, as well as how to define how exact such approximations should be. Instructors also spend some time discussing how calculus is used in neural network training before showing you how it is used in linear regression models.

This course is designed to provide you a basic grasp of calculus as well as the vocabulary you'll need to hunt up concepts on your own if you get stuck. Without getting into too much depth, hopefully you'll have enough confidence to take some more specialized machine learning classes in the future.

Skill you will gain

• Linear Regression
• Vector Calculus
• Multivariable Calculus
• Gradient Descent

Instructors: Samuel J. Cooper, David Dye, A. Freddie Page

Coursera rate: 4.7/5.0, 5.120 ratings

Offered by: Imperial College London

Enroll here: https://www.coursera.org/learn/multivariate-calculus-machine-learning