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Back to Mathematics for Machine Learning: Linear Algebra

Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London

4.6
stars
12,550 ratings

About the 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

GB

Aug 17, 2020

The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.

NS

Dec 23, 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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1751 - 1775 of 2,480 Reviews for Mathematics for Machine Learning: Linear Algebra

By Quynh A T

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Mar 16, 2020

very well

By Sreeprakash N

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Nov 11, 2019

Very good

By Prabal G

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Oct 10, 2019

very good

By Jorge H L B

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Oct 8, 2019

Excelente

By Greg H

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Jun 19, 2019

Very Good

By Sasikumar G

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May 23, 2019

Excellent

By Colin W

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May 16, 2019

Excellent

By Fabrice M

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Apr 15, 2019

Excellent

By Xinya X

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Mar 13, 2019

I like it

By DIVYANSH S

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Feb 4, 2019

excellent

By Edward K

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Sep 18, 2018

very nice

By 海鹿

•

Mar 17, 2018

excellent

By Tanuj

•

Nov 1, 2025

all good

By Karta K

•

Mar 18, 2023

Amazing!

By Nicolas C

•

Feb 2, 2023

Awesome!

By Sukhvir S

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Aug 23, 2020

NIce One

By HALIKHEDE S S

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Aug 20, 2020

best one

By Isaac M M

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Jul 24, 2020

Excelent

By Yoshiaki K

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Jun 9, 2020

Awesome!

By MOHAMMED F R

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May 30, 2020

its good

By Deleted A

•

Jan 26, 2020

Perfect!

By Xiaxin

•

Oct 5, 2019

可以听得懂的课程

By Kanishka A

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Apr 24, 2025

Amazing

By Võ H K K D

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Mar 27, 2025

So good

By Insyiraah O A

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Mar 21, 2022

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