The course objectives, aims, and motives were very clear but after mid week 3, the teaching became abstract and the professors should have given little more explanation about the advanced topics.

I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm. There should be more explanation to how the math works or comes to its formula.

The assignments kill this course, great instructors, and pace, in my opinion. (I am a beginner in linear algebra and I understood the concepts without needing Google or external resources)

I think the course is more suitable for those who have had comprehensive theoretical knowledge in linear algebra and intend to learn more about its practical use and its relevance to code.

The course is good. But the last module (week) is not so good. I think that the explanation of the Page Rank algorithm is not very good. I also think that the final test is very confuse.

Overall course was good, I have learnt few new concepts which I haven't know till now. But at the end, things were not clear while putting all together for solving page rank algorithm.

Linear Algebra content is great, however, was not aware that a huge portion of grade is based on Python programming exercises!!! Only need to learn Linear Algebra, not programming!!!

Intuitive geometrical representations of eigenvalues and eigenvectors in 3blue1brown style. Had some concerns with a few theoretical inaccuracies of the material presented.

It was a good to review linear algebra again and see how what I learned in my first year course at university could be applied here! Plus it was good to see Python again.

The course feels very disorganized in general. Some quizzes are about 10 standard deviations from the average difficulty, which is befuddling to say the least.

It is good course for machine learning. But I didn't fully understand the page rank system with damping.

More explanation of damping is needed for the newbie.

Transition from explanation of basic to advanced concepts could have been better. There was an assumption that few things was already know to the learner.

Not enough focus on how material connects to machine learning. A case study example would help, as would a very slow, detailed step-by-step illustration.

Nice one. But realized I needed more foundation on this matter. So decided to abandon and level up my topic knowledge in Khan Acadamy. I will be back.

Good for someone who has bit background in Linear Algebra and Python. I won't recommend this work for a completely newbie as this course lacks depth.

The video lecture skipped so many important concepts and difficult to catch up. I sometimes refered to other textbooks to understand the lecture.

I think this course is a little bit hard for a beginner with python. And I hope that the teacher can talk more about the Machine learning part.

Excellent videos with generally appropriate pace. Some more examples and exercises would probably improve the learning experience even more.

Very good explanations on difficult subjects but a bit short coverage of various cases, thus some assignments and quizzes were challenging.

Answers to Several questions in Week 5 quiz around eigen values and eigen vectors need to be revisited as they donot appear to be correct.

This course consists of very fundamentals of linear algebra. I expected advanced linear algebra contents and more software applications.

Sadly too many vital exercises are behind the premium path with -- with no way to complete the course properly without access to them

feels like the important/interesting stuff was described briefly, while deep diving into calculations.

I miss the broader picture.

The course gives a good beginner-friendly Introduction to Linear Algebra. But the courses could cover a little more topics in LA.