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

11,467 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


Sep 9, 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.


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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2126 - 2150 of 2,269 Reviews for Mathematics for Machine Learning: Linear Algebra

By Denys H

Aug 6, 2022

I searched a lot of additional information in order to understand something. This is the most disadvantage of this course. Nevertheless, the course have many exercise and labs, which were interesting.

By G V

Jan 23, 2022

The course objectives, aims, and motives were very clear but after mid week 3, the teaching became abstract and the professors should have given little more explanation about the advanced topics.

By Meng H P

Feb 1, 2020

I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm. There should be more explanation to how the math works or comes to its formula.

By Santiago R R

Jun 20, 2020

The assignments kill this course, great instructors, and pace, in my opinion. (I am a beginner in linear algebra and I understood the concepts without needing Google or external resources)

By Rong D

Aug 30, 2018

I think the course is more suitable for those who have had comprehensive theoretical knowledge in linear algebra and intend to learn more about its practical use and its relevance to code.

By Marcus V C A

May 23, 2021

The course is good. But the last module (week) is not so good. I think that the explanation of the Page Rank algorithm is not very good. I also think that the final test is very confuse.

By TirupathiRao p

May 16, 2020

Overall course was good, I have learnt few new concepts which I haven't know till now. But at the end, things were not clear while putting all together for solving page rank algorithm.

By David D

Aug 18, 2020

Linear Algebra content is great, however, was not aware that a huge portion of grade is based on Python programming exercises!!! Only need to learn Linear Algebra, not programming!!!

By Aurel N

May 8, 2020

Intuitive geometrical representations of eigenvalues and eigenvectors in 3blue1brown style. Had some concerns with a few theoretical inaccuracies of the material presented.

By Akeel A

Jul 22, 2020

It was a good to review linear algebra again and see how what I learned in my first year course at university could be applied here! Plus it was good to see Python again.

By Manuel M

Jan 25, 2019

The course feels very disorganized in general. Some quizzes are about 10 standard deviations from the average difficulty, which is befuddling to say the least.

By itwipsy17

Feb 25, 2020

It is good course for machine learning. But I didn't fully understand the page rank system with damping.

More explanation of damping is needed for the newbie.

By vignesh n

Sep 12, 2018

Transition from explanation of basic to advanced concepts could have been better. There was an assumption that few things was already know to the learner.

By Alexander D

Aug 7, 2018

Not enough focus on how material connects to machine learning. A case study example would help, as would a very slow, detailed step-by-step illustration.

By Santiago M

Sep 14, 2020

Nice one. But realized I needed more foundation on this matter. So decided to abandon and level up my topic knowledge in Khan Acadamy. I will be back.

By Sanyam G

Apr 3, 2022

Good for someone who has bit background in Linear Algebra and Python. I won't recommend this work for a completely newbie as this course lacks depth.

By 川上孝弘

Aug 16, 2022

The video lecture skipped so many important concepts and difficult to catch up. I sometimes refered to other textbooks to understand the lecture.

By Cindy X

Dec 20, 2018

I think this course is a little bit hard for a beginner with python. And I hope that the teacher can talk more about the Machine learning part.

By Felipe G W

Sep 12, 2022

Excellent videos with generally appropriate pace. Some more examples and exercises would probably improve the learning experience even more.

By Christos G

Jan 24, 2021

Very good explanations on difficult subjects but a bit short coverage of various cases, thus some assignments and quizzes were challenging.

By Atish B

Sep 24, 2020

Answers to Several questions in Week 5 quiz around eigen values and eigen vectors need to be revisited as they donot appear to be correct.

By Serdar D

Feb 15, 2021

This course consists of very fundamentals of linear algebra. I expected advanced linear algebra contents and more software applications.

By Amal J

Jul 15, 2020

The course gives a good beginner-friendly Introduction to Linear Algebra. But the courses could cover a little more topics in LA.

By Jorge G

Aug 14, 2020

I would give it 3.7, examples are good but the vectors the lecturer draw were no easy to understand because of drawing by hand.