Excellent course. I just wish the assignments were a little harder. The last assignment was the perfect toughness level. Made me connect concepts, look up stuff and actually get out of my comfort zone to learn.

The course did reflect the fundamental concepts in an excellent way. The only thing which frustrated me was " The PageRank" assignment and how was it even related to this course? Anyways, highly recommended.

I used to think that where are we going to use these matrices eigen values and vectors in real.. and I've got my answer from this course...Thanks to the professors who had given clear view on these topics...

Great instructors, excellent content. I would like to see more practical use cases of the material (at least as a self-study reading). And please add an explanation behind formulas for the eigenvectors part.

Fast paced linear Algebra, perfect to get refreshed. Might be too concise for those who learned Math not in English a few decades ago, will require to browse Forum for additional links to read on the side.

Great primer. Covers most of the important themes of LinAlg needed for applying machine learning, and also provides really good intuition. Useful for those wanting to sharpen up before further study of ML.

Good content, animated and visually-appealing lectures - considering it is mathematics, assignment material and quizzes are helpful for review, but minimal support and feedback for questions or problems.

It's a great course as a refresher, but for mostly folks with a lot of time. The assignments are fair, but sometimes it's dive-in kind of stuff, whereby the assignment itself is the instructive example.

An outstanding course which builds your mathematical intuition rather to prepare you for mathematical calculations. My opinion is that its contribution is significant in the pool of courses in coursera.

a bit rush course covering the most important part of linear algebra, give me a very good intuition other than mathematic notations! the course might be better to add some explaination on math side!

even though my code was right in the last assignment the grader kept getting timed out. it took 3 days to work and in the end the code was same. the course on the other hand was quite good and easy.

Great content and direction. Only negative is the sometimes frustrating experience with the Jupyter Notebooks: debugging what has gone wrong is very difficult, due to a lack of good error messages.

Be careful as a beginner in coding. It might be frustrating from time to time. I have spent the majority of my timing on the coding . At the end worthwhile, but did not feel that way at that time

Good, but sometimes it is neccessary to look for supporting materials. I took this course in combination with MIT course in LA and this offered another, more practice oriented, view on the topic.

The course is a great resource to brush up on the fundamentals of linear algebra and learn about the meaning behind the math.It prepares people for any further courses which use linear algebra.

This course was very insightful. The instruction was well done with expressing the intuition, but the process was left vague on a few concepts and required me to look up worked out examples.

Adding more examples would have made this course better. I got confused during some assignments because it was the first time I applied the concepts and did not know what I was doing wrong.

I think it is a great course. It is definitely not for beginners and I feel the lectures are somewhat rushed on some videos. It is best to view this course as a supplement and not as a main

good videos and good instructors; programming assigments could be a little bit clearer in the instructions. Overall good for understanding the maths behind Machine Learning. Thank you!

The course was good but it could have been better if the exercises had more difficult questions or probably a section with more difficult questions using the concepts that were taught.

Great course, a lot of visulizations to show you what linear algebra do and why do we need it. The professors really make the course content interesting and the course flow is great.

This is excellent course provided as fundamental skills required in Machine learning journey. I was excited to be learn from best lecturers ever and, thanks to Imperial College.

1.Need more clarity on calculating Eigen vectors using back substitution of Eigen values.

2. Power Iteration method for the Page Rank Algorithm should be more specific and clear.

The course can help me more clearly when approach some algorithms in the optimize function of Machine learning. Thanks coursera and Imperial London College about this course.

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A really well structured course, a few minor problems with the coding assignment formatting but otherwise all the topics were covered in depth with plenty of application tests.