Back to Mathematics for Machine Learning: Linear Algebra

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11,475 ratings

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....

PL

Aug 25, 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.

HE

Aug 8, 2021

the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering

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By Zhuocheng Y

•Dec 2, 2018

The programming grading system doesn't work well, but the course is great anyway

By Xinsong D

•Jun 14, 2018

Excellent, but for the pagerank part, the instructor teaches a little bit fast.

By Rocber

•Sep 30, 2018

it is really useful to help me build geometric meaning with vector and matrix.

By phanidhar s

•Jun 1, 2020

The course will help us to apply the matrices for machine learning algorithm.

By Benjamin W

•Jul 16, 2021

Some of the autograding wasn't great but the discussion forums helped a lot

By Tarun A

•May 21, 2020

after learn from this course, i know very well about machine learning

thanks.

By Aviral A

•Dec 10, 2018

A good course for gaining knowledge for Linear Algebra for machine learning.

By 胡与诚

•Apr 2, 2018

Good course, But I think it should explain more about the underlying things.

By Alfian A H

•Mar 3, 2021

Overall is good. But somehow, there are parts that I don't understand well.

By Bruna R

•Feb 6, 2021

the course is excellent. I have learned a lot and it was very interactive.

By Gundepudi V

•Jul 13, 2020

Was a but fast. For non engineering or people who are new it will be tough

By Abhirup B

•Jul 29, 2020

goood course well designed qizes and aasignments time saving yet fruitful

By Julian A

•Jun 12, 2020

Fantastic course that provides a great introduction into linear algebra.

By Ivan

•Jun 4, 2018

The course content is good, but the programming assignment is too easy.

By Ritik j

•Jun 1, 2020

some topics are explained in a typical way and a bit problem was occur

By Nugy

•Feb 24, 2021

The eigen value and eigen vector courses are a bit hard to understand

By Kevin O

•Feb 25, 2021

A good refresher with some really useful insights about eigenthings.

By KIRANKUMAR M

•Jul 21, 2020

Its is the best course to know about matrices and their applications

By KURRA S S 2

•Dec 26, 2022

It is so useful to me for getting the knowledge in machine learning

By Sharad K L

•Mar 9, 2020

Exams were hard and most of the exams were source of the knowledge.

By JOSÉ M B D

•Jan 25, 2020

excelente curso, me gustaría que se complementara con programación.

By Jihun Y

•Jan 3, 2022

teaching essential, doesn't cover many topics, but well curated!

By Sai V P

•Aug 14, 2020

Decent course. Wish things were explained in a more detailed way

By Ng Y Y

•Jun 21, 2019

Good overview and introduction to key concepts of linear algebra

By Mohammed K A

•Feb 12, 2022

need some use of visualisation in some topic like change basis

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