The virtual course "Mathematics for machine learning: multivariate calculus - Virtual Course - Coursera", is a course with different contents and offers video classes of Approx. 18 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
This course provides a brief introduction to the multivariate calculus required to build many common machine learning techniques.
We start at the beginning with a review of the formulation of a "rise over run" slope, before turning this into the formal definition of the gradient of a function.
Then we start building a set of tools to make the calculation easier and faster.
Next, we learn how to calculate upward pointing vectors on multidimensional surfaces and even put it into practice through an interactive game.
We take a look at how we can use calculus to build approximations to functions, as well as help us quantify how accurate we should expect those approximations to be.
We also spend some time talking about where calculus comes from in neural network training, before finally showing you how it is applied in linear regression models.
This course is intended to provide an intuitive understanding of calculus as well as the language to look up concepts on their own when they get stuck.
Hopefully, without going into too much detail, you'll still come away with the confidence to dive into some more focused machine learning courses in the future.
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Imperial College London
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