This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you 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 future.
About this Course
Skills you will gain
- 5 stars76.68%
- 4 stars19.13%
- 3 stars3.17%
- 2 stars0.65%
- 1 star0.33%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: MULTIVARIATE CALCULUS
Very clear and concise course material. The inputs given during the videos and the subsequent practice quiz almost force the student to carry out extra/research studies which is ideal when learning.
Multivariate Calculus course has been by far one of the best moocs I have taken in relation to learn maths for ml, its foundations helped a lot to understand how everything relates from bottom up.
I am happy see that how the simple concepts of calculus can be helpful in answering the machine learning problem. Instructors are very professionals and did full justification with the course.
Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.
About the Mathematics for Machine Learning Specialization
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