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.

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!

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)

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.

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!!

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.

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.

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.

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!

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).

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.

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.

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!

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.

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.

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.

This is one of the best math courses I've took in Coursera.

20 years ago, I took linear algebra in my University. And now I needed to remember some of these topics. For this purpose, it is excellent. The way matrix transformations is addressed was new to me and make the topics very easy to understand.

Unfortunately, the topics of week 5 (eigenvectors and eigenvalues) were not so well taught as the topics of weeks 1 to 4.

An excellent visuals of application of Linear Algebra. Some of the homework is not based on the material presented in the class, and requires you to go to external sources to learn more. When doing homework, sometime, it unclear what additional material you don't know and need to learn outside of the course work, to solve the homework. Other than homework, it's an great class to take! I've really enjoyed this class!

Python. Python is needed. It'll be very hard to progress without that. Otherwise, the course is great. Instructors are good. Week 5 is sort of a stretch. The Page Rank Algorithm is not really explained well (some say that it is not explained completely) and dedicating an entire week to just eigenthings does not make a lot of sense. The exercises are good though. A good introduction to the basics of Linear Algebra.

Video lectures are great and really help with understanding why you are learning the material and what the concepts mean. The programming assignments and quizzes are challenging. There were some cases where I did not understand the quiz question and did not know why I got it wrong or even correct if I guessed. Access to solutions after completing a graded assignment or even after the course would be beneficial.

It's quite good. The material implies they are aiming to teach linear algebra and basic Python programming basically from scratch, but it goes over topics so quickly and skims over so many details that I suspect this course only works if you've studied much of it before. Even though I have studied much of this before I still had to go and find other sources of information, as the explanations here are so brief.

Generally great course. Explanations are very clear. Cons: ① no textbook/slides/reading materials etc., have to take notes and screenshots for every single thing you want to record. ② The content is not enough. Way too less knowledge covered than college-level linear algebra course. I took this course to refresh my knowledge and it turned out that more than half of the contents are the ones I still remember.

The course did a good job in building up intuition about linear transformations and change of basis. It's a useful review of the core of linear algebra and can be implemented in many fields. The programming assignments were very basic, so maybe a bit more challenging ones will be better. I didn't like the last module of the course about Eigen analysis. While the topic is very useful, it was a bit rushed.

Eigenvalues and eigenvectors while explained conceptually very well, the jump to page rank and transformations using them was bit hand wavy. May be it is not that important or the topic is too complex. I think I have to go through it multiple times to get the gist of it once again. I might if there is real applciation of it in ML.

The course is still very good and thank you for sharing it with us.

Without having taken a Linear Algebra course previously, I thought the course was worthwhile to introduce topics. However, I had to spend ample time researching outside of the course IOT learn. In addition, although I was introduced to many LA topics, I still need additional practice in grasping certain concepts. This course is a good introduction to topics you'll have to research on your own.