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

4.7
stars
11,932 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

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

C

Mar 31, 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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2151 - 2175 of 2,363 Reviews for Mathematics for Machine Learning: Linear Algebra

By Md H R

Dec 19, 2020

Good course

By Ananda U

May 27, 2020

Nice Course

By praneel a

Jul 7, 2021

very nice

By Zala R

May 26, 2020

fantastic

By KRAKOU D G S

May 23, 2020

very good

By Sharob S

Mar 4, 2019

Loved it.

By EL O A

May 20, 2018

Very nice

By NITESH J

Jul 5, 2020

TOO long

By TAVVA G M

May 17, 2020

good one

By Thita A I S

Mar 2, 2021

thank's

By Millati A L

Mar 25, 2021

yesss

By G A N M

Oct 14, 2020

Good!

By Deleted A

Sep 27, 2018

Good!

By Gilang M

Mar 21, 2023

Good

By venkatadurga P

Sep 13, 2021

good

By Persis

Jul 18, 2020

gfhf

By Zhassulan S

May 24, 2020

Good

By Ishan Y A

May 19, 2020

nice

By Li J

May 20, 2018

nice

By Reed R

Jul 14, 2018

The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e. (a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools. While I appreciate this goal and enjoyed the early foray into projection, I never felt the "a ha" moments I did as an undergrad in a class that used Gil Strang's "Introduction to Linear Algebra" (which I reread alongside this course as a supplement). The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course. It could be that I was just looking for a more complete treatment of the subject than the course ever intends to offer, but I strongly felt that with a bit of restructuring, that the subject could be presented primarily intuitively, but with a level of clarity and artfulness in its conclusion that will ensure that students remember the core concepts beyond when they remember its presentation.

By Michael W

Apr 29, 2023

I think this course is a fair introduction or refresher on several linear algebra concepts, and I appreciate that the course was designed to focus on those concepts relevant to understanding and working with Machine Learning in particular. However, I found the second lecturer was harder to follow than the first. At least for me and my needs as a student, he seemed to rush through complicated ideas, leaving me to pause and spend extensive time trying to find on the internet the answers to what confused me, and then he would go slowly and thoroughly through ideas I found trivial. The first lecturer had only one instance of glossing over a crucial intermediate thought in moving from one idea to the next; in contrast each lecture provided by the second lecturer had multiple such instances.

I may have been particularly unlucky, but I also found the forums unhelpful. I found many entries for each question I had, and yet I didn't find any of the discussions or the rare answers within illuminating.

All my complaints may be the results of peculiarities in how I think, but I believe the course could do with some revision in the last two modules.

To the second lecturer's credit, he is very charismatic. I just wish he had anticipated what concepts would need more thorough explanation.

By Eitan A

Jan 12, 2020

As of this writing, I am almost done with week 4 of Mathematics for Machine Learning: Linear Algebra. The content of the course is excellent and professor David Dye's lectures are to be commended no doubt. The reason for my low rating is because the programming assignments are broken and that's really not acceptable for paid offering such as this. To clarify, at various points throughout this course, students are asked to complete a programming assignment. The student is presented with a button which says, "Open Notebook". The student is supposed to click this button and be redirected to a Jupyter Notebook (and interactive Python execution environment). Unfortunately, instead of being redirected, click on this button results in a "404 Not Found" error. There are various discussions in the class discussion forum regarding this issue (some months old), but no action has been taken to resolve this issue. Luckily, someone taking the course managed to find the programming assignments and posted them on google docs for others to use. I've been working these which is fine, but as I said, we're paying for these courses, someone should be resolving this.

By Maprang S

Jun 16, 2020

I never took Linear Algebra in university. The last time I got exposed to this topic was more than 10 years ago when I was still in junior high. This course is very condensed. Each video covers each topic relevant to ML very briefly and the instructors go very fast on explaining each topic. This means students have to do a lot more research on their own to really comprehend the concepts. What's nice about this course is the programming assignments. They give you a chance to apply math concepts to the computational model. Something like this you wouldn't have a chance to do if you don't spend on an online course like this one, I guess. Overall, I think this course provides values in a way that gives you an overview of how Linear Algebra is used in ML. For me personally, I know I still need to consult other sources online to further understand Linear Algebra as I'm not sure that after finishing this course I've got adequate knowledge to pursue ML. What all that said, hence I give this course 3 stars.

By Jacque G L

Sep 13, 2022

It was challenging to get through the quizzes and assignments. The video lectures do not cover all the material needed to complete the quizzes and assignments. I had to search for and learn materials on my own. I learned a lot and enjoyed the topics covered. I would have given this a higher rating if I had known more explicitly that I would need to reference outside materials. This learning style took a lot of time, and I had to digest the material for a day for the information to make sense. Then the next day, complete the quizzes, assignments, or labs. The Python part was easy and a minor element of the course. I completed the course in about 16 days; perhaps that was too much new material to cover in a short time.

By Khai T

Feb 20, 2022

I personally do not recommend this course for anyone who has not had any prior knowledge in linear algebra. Although it states that this course is for beginner, you should have a (quite) decent background in linear algebra to understand all the materials. The geometric intuition parts of projection and eigenproblem are interesting. I would suggest the instructors to prepare some lecture notes for each week, instead of giving just a single cheatsheet at the end of the course. This would help learners to review the material better. Besides, some videos do not have the subtitle synced. Thus, matching between the full transcript and the video is quite hard for non-English speaker.