Chevron Left
Zurück zu Mathematics for Machine Learning: Linear Algebra

Bewertung und Feedback des Lernenden für Mathematics for Machine Learning: Linear Algebra von Imperial College London

11,332 Bewertungen

Über den Kurs

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....



22. Dez. 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.


25. Aug. 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.

Filtern nach:

2051 - 2075 von 2,238 Bewertungen für Mathematics for Machine Learning: Linear Algebra

von praneel a

7. Juli 2021

very nice

von Zala R

26. Mai 2020



23. Mai 2020

very good

von Sharob S

4. März 2019

Loved it.

von EL O A

20. Mai 2018

Very nice


5. Juli 2020

TOO long


17. Mai 2020

good one

von Thita A I S

2. März 2021


von Millati A L

25. März 2021


von G A N M

14. Okt. 2020


von Deleted A

27. Sep. 2018


von venkatadurga P

13. Sep. 2021


von Persis

18. Juli 2020


von Zhassulan S

24. Mai 2020


von Ishan Y A

19. Mai 2020


von Li J

20. Mai 2018


von Reed R

14. Juli 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.

von Eitan A

12. Jan. 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.

von Maprang S

16. Juni 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.

von Jacque G L

13. Sep. 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.

von Khai T

20. Feb. 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.

von Avinaash S

9. Sep. 2020

The lecture material in this course is great, and the quizzes are a lot of fun and it provides good resources for learning. However, the programming assignments are a pain due to lack of guidance and the grades are penalized due to minor things like indentations as opposed to actual math errors. This isn't a python course, its a math course, and grades should be awarded and penalized based on the math skills one has acquired throughout the course, not on the programming or whether an indentation is off. I highly recommend the course to learn linear algebra but I strongly encourage the instructors to improve the programming assignments or alter the assessment methods.

von MR T

24. Apr. 2020

It must be difficult to pitch the level of these courses.

I have been taught Data Science whilst on an apprenticeship but didn't feel the maths was taught rigorously enough and hoped this would fill gaps of in knowledge.

The breadth of the concepts covered on the course achieved that but a lot of research was required from other resources to clarify certain topics which is why I think a beginner rating for this course might not be fair.

If you are not confident with maths, this course is achievable but expect to devote time to on other sites.

The PDF supplement is concise but useful for reference

von Anne R

18. Jan. 2022

This course was helpful in reviewing topics in linear algebra and focusing on the usefullness of projections and eigen problems in manipulating vectors. If you have taken a linear algebra course previously you should find the course pretty easy. If you have not taken a linear algebra course before, this course may be much more difficult to complete unless you are prepared to use other resources to help manage the course topics and assignments. I was hoping for more challenging course assignments but understand that much of it is self-assessing what you are learning as you go along.

von Constantin N

1. Aug. 2022

The topic is presented really interestingly and encouraging, but then it becomes very cluttered or very abstract. The worst part is that after some 5 minute video about one concept you're supposed to answer a quiz with calculations you haven't even gotten the formula or an example. Those problems are mentioned several times in the dicussion board. It's a shame since the teacher sincerely likes this topic but fails to convey it in a illuminating manner.

If you're up to look up other examples after every lesson, this course is okay, but it's not worth the 50 € subscription.