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Back to Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Linear Algebra, Imperial College London

4.6
2,585 ratings
455 reviews

About this Course

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

Top reviews

By PL

Aug 26, 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

By CS

Apr 01, 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

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454 Reviews

By Tirthankar Banerjee

Apr 20, 2019

Excellent intro to Linear Algebra with clarity on concepts such as application of Gram Schmidt method and Eigenvectors.

By Marc Pfander

Apr 19, 2019

Excellent course to refresh linear algebra basics, build intuition and see the subject from a machine learning perspective. I wouldn't recommend it for people that are new to the subject, since the pace is fast, much is omitted and the assignments aren't always easy. Every now and then, the calculations come before the intuition, which can be tricky to follow. However, most of the course is very didactic and the combination of videos and challenges kept me motivated throughout.

I suggest the youtube channel of 3Blue1Brown whenever you feel lost with the subject at hand.

By rasheeq ishmam

Apr 19, 2019

Should go more in details.

By Yutong Zhang

Apr 17, 2019

So great in general! But since it is not a pure maths course, some concepts are not explained in depth. It's a perfect course for self-learner because you can always go to the forum to look for answers.

By Fish

Apr 16, 2019

Very good I learn a lot though I get confused in Week 4 about E @ TE @ inv(E). Thank you profs!

By Ivan Kravtsov

Apr 14, 2019

Great instructors and great engagement. A very comfy way to have a broad view on a linear algebra.

By

Apr 14, 2019

Excellent

By Anuj Nagpal

Apr 11, 2019

The course gives you all the intuition behind all the major linear algebra concepts one needs to know

By João Carlos Lima Selva

Apr 11, 2019

The course is very good, almost perfect for my purposes. I liked specially the effort to make the students get the necessary intuition instead of pushing a lot examples as many other MOOC usually do. But I've noticed some negative points. I ask you to take my critic as a sincere effort to improve the course and eliminate some mistakes that really matters to the students. The last quiz seems quite disconnected with the lectures and there isn't a support guide or tutorial not even a mentor answering the questions in the week 5 forum. Some mistakes on videos (eigenvalues and eigenvectors) were confirmed by the lecturer but never corrected. Not even a errata on resources section. Talking about the resources, I think it is very poor. Cousera has many better examples.

By Santosh Bangera

Apr 10, 2019

A well designed refresher course with greater emphasis on teaching us the underlying concepts.