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

10,852 Bewertungen
2,166 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....



31. März 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.


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.

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1626 - 1650 von 2,179 Bewertungen für Mathematics for Machine Learning: Linear Algebra

von Daniel R

12. Aug. 2018


von Deepak K A

19. Juni 2018


von Joseph S

27. Sep. 2021

von Rayanne

21. Okt. 2019


von Tushar S

27. März 2019


von John F

23. Apr. 2020

It's a good overview. I think that to get a lot out of this course it would help to have at least encountered basic matrices, vectors etc before. It's not that these concepts aren't introduced it's just that I can imagine if you have never encountered these things before you might get overwhelmed a bit quickly. It would also help if you have some rudimentary knowledge of programming i.e. know basic syntax, what a for loop or a while loop is and other basics. I know a bit of programming and i'm pretty ok at math so the course was manageable for me. Especially good was showing how all of the concepts learnt can be applied to understanding the Google Page Rank algorithm.

The best part of this course is the conceptual overview it gives and the instructors constantly reiterate how this type of understanding is more important than just being able to chug through a whole lot of algebra. Computational skills aren't really that important because apart from very basic examples, a computer is pretty much necessary to do the calculations anyway and as we all know, just because you know how to plug stuff into a formula doesn't mean you have the faintest idea what you are actually doing!

I think a very bright person could probably fully understand this course coming at it from scratch but I know that I would have struggled if i'd never glanced at the math or done some basic programming before.

von Vern

10. Apr. 2018

I would give this course 5 stars for the fact that in five weeks, the course is able to go through perhaps a semester or two or three of Linear Algebra (LA), and how LA fits into data science. I gave it four stars because I believe the program should include lots of links to reference and learning aid resources. Because I had done a couple other courses on LA relatively recently, some these arcane LA concepts were grasped with some, but not too much, effort.

If you are even just a little familiar with LA, this course will give you a good foundation for the LA relative to data science. So, if this is you, and you want to get into Machine Learning (ML) to understand how ML works internally, then jump right in.

Thanks to all who contributed to make this a great ride.

von Vy H

10. Sep. 2021

Despite having learnt about vectors and matrices in the past, I still find this course challenging at time due to incompleteness of lecture contents. But researching and thinking through these issues did help me better understand the course material. The instructors have a different view on teaching maths in the age of computers. Instead of focusing on solving equations, the main focus of the course is on building an intuitive understanding of the mathematical concepts. And they deliver on this promise. I also appreciate the effort to select only materials relevant to ML. This saves students lots of time and effort.

von John G

30. Sep. 2018

Overall, the course is good and well worth your time if you goal is to brush on Linear Algebra. It is pretty important that you have been exposed to linear algebra before though, as some topics are covered pretty quickly. My only complaint is that there was a lot of unnecessary obfuscation. The lectures constantly alluded to things without actually naming them (e.g., gradient descent in one of the earlier lectures). I found the "Essence of Linear Algebra" video series on YouTube to be invaluable to actually making sense of some of the lectures in this course, so if you do take this course I suggest doing the same!

von Sagnik B

7. Juli 2022

The course provides an excellent opportunity to go through challenging problem sets, quizzes, and assignments. This not only boosts your critical thinking and problem-solving ability, but also instills resilience and fortitude - struggling through problem sets can be very arduous, but having the mentality to get through it is what the core of learning is. However, despite all these benefits, I would say the course instructors often rushed the concepts and had unclear explanations (perhaps I lacked the mathematical insight to see what they were saying, but I was often confused with what they said at times).

von sreekar

13. Sep. 2018

For purposes of learning (refreshing) linear algebra for machine learning, this course is a great tool. There were some blips here and there, where the explanations are lacking , but overall a good resource. i have to add that this combined with 3Blue1brown LA series provides optimal learning than the course alone. 3Blue1Brown provides better intuition while this course will walk you through the more rigorous math part of it. for best results ofcourse you may have to do solve lots of Text book problems yourself. (there is a recommended text book, but not necessary for passing grade or completing course)

von Domenico D F

12. Juli 2022

E​xcellent course, give a fast and intuitive understanding of the concept that are below linear algebra. For people doing other kind of work, like phD in some other non-mathematical field, that somehow lack the mathematical back group I think is good place to start. Still need to see how those skill will test against the real world. But time wise is probably one of the best option. I also started with a book, Linear Algebra done right, but, even thou the concepts are way more deep in a book, I found that was taking me too much time to arrive at a reasonable understanding. So I opted for this course.

von Tim Z

31. März 2018

Actually I have learned linear algebra before but forget a lot, so I want to use this course to review it and help me prepare for the study of machine learning. As a computer science student(sophomore), I think the coding assignments are too easy for me. But it indeed introduce the core concept and equations in linear algebra, which are quite useful for future learning. In my opinion, freshman students or students just graduated from high school may feel more satisfied with this course. And it is better for students who have learned linear algebra before to find harder courses to learn.

von Harsh S

25. Juli 2018

Good course for your first step into machine learning. Very engaging. Great application with the PageRank problem. The course does dive into the core understandings of linear algebra and you do develop a sound mathematical intuition. The coding isn't very difficult and has been eased for non-programmers. However, what happened for a keen learner like myself is that I wrote code which I didn't understand very well myself, but still managed to get a full score. This made me a bit uneasy. Otherwise, an awesome course to get started on and it is possible to finish in two weeks.Enjoy!!

von Marc P

19. Apr. 2019

Excellent course to refresh linear algebra basics, build intuition and see the subject from a machine learning perspective. I wouldn't recommend it for people that are new to the subject, since the pace is fast, much is omitted and the assignments aren't always easy. Every now and then, the calculations come before the intuition, which can be tricky to follow. However, most of the course is very didactic and the combination of videos and challenges kept me motivated throughout.

I suggest the youtube channel of 3Blue1Brown whenever you feel lost with the subject at hand.

von Kenny C

10. Juli 2020

The lectures from the instructors were well-thought-out and planned, which gave it a clear line of reasoning and allowed them to give good examples. However, some parts of the content are glossed over, especially with the introduction to matrices. The instructor does not define what a matrix is and how basic operations with matrices work, though this is done when they introduce vectors. Overall, I would have liked a more rigorous and mathematical explanation of the content, but the course does do a good job of building an intuitive understanding of the topics.

von Hamed K

22. Apr. 2020

The instructors explained the topics almost clearly. I would say the assignments were a bit difficult and did not have any explanations by instructors in the videos. More importantly, the support of instructors in the forums is very poor. Many people have had issues but there has not been any answer provided by the instructors. The submission of the assignments were a nightmare since grading of the coding gives a lot of issues even if the assignment is correctly done. With more support by the instructors in the discussion forums could be a much better course.

von Kjell E N

20. Mai 2020

This was a really good refresher for material I learned long ago. The instructors were enthusiastic and engaging, and the production quality was good. The material is pretty abstract, however, and as with any brief course, it is difficult to develop a truly deep intuition about the subject without much further study and practice. I like that the homework was quantitative and required me to think carefully and practice the mechanics of doing the math. If nothing else, taking this course has me wanting to keep studying Linear Algebra!

von Kenneth B

9. Jan. 2021

This course provides a good overview of the basics of linear algebra for machine learning tasks. I recommend the class and I'm glad I took it, but it could be improved by adding more thorough explanations for quiz answers. This would have let me better understand how to arrive at the solution to a problem.

I didn't have any Python experience coming into the class and made it through OK, but there were times where this lack of knowledge interfered with my ability to complete the Python labs (particularly the last one).

von Osaama S

29. Sep. 2019

Instructors have done a really good job at introducing the fundamentals specially from a graphical point of view which allows you to build your grasp strongly around the topics in a way that is not accomplished in a traditional college classroom. However, I would say perhaps there could be more challenging questions on the real world applications of linear algebra in machine learning followed by in-depth step-by-step solutions in order to really get the application-based learning inside your meat.

von Gabriel L S

12. Aug. 2019

I like the the structure of explaining the theory using examples (in this case, geometric/visual examples). However, I would love to have further understanding on the basic linear algebra topics (or at least be linked to websites that explain this further) to allow flexibility to students like me who has zero knowledge on linear operations. Overall, I was able to overcome the challenged through self learning, understand the concepts well, and appreciate the applications in machine learning.

von Allan d Q

13. Nov. 2020

I'll go through this one once again, I feel like I'm not fully comfortable with the content. I had to use loads of external resources in order to pass through the quizzes and some of them I guessed, I must say.

The coding ones were a bit easier since I'm a software engineer already but yeah, I definitely need to through it again but overall, I've discovered many new things that were unknown unknowns.

Thank you all for putting this kind of content online for everyone! Outstanding job!

von Yuriy G

31. Jan. 2019

Thanks a lot for this course! The explanations and lecturer work were brilliant!

It's very good for introduction but it lacks a strict wording, definitions and some generalizations. Especially in the section of changing basis. When after considering a number of examples, I really want to move on to the general case.

Anyway, you've a made a great work because anyone without any preparation can get acquainted with very deep mathematical ideas.

von Kumar S

14. Apr. 2020

This is a very good course and helped me a lot in getting started with going through mathematical concepts of machine learning. It has taught me lot oh Linear algebra stuffs in a very intuitive way. However the quality of assignments could have been better and some of the concepts that were important and needed more explanation was skimmed through rather quickly. However, I am really satisfied with the overall learning that i got.

von Nikita V M

6. Feb. 2020

Excellent instructors and video quality. Some frustrating elements with assignments being either somewhat unclear or redundant. The only severe flaw was that grader feedback was entirely pointless, as it made no effort to even give an example of what went wrong. Simply saying that something was incorrect without providing, say, an example of the input matrix that failed, in no way helped advance my understanding of the problem.