Nice course, with many insights. Sometimes the topics are given too quickly, I would have rather preferred less arguments but discussed more thoroughly. Nevertheless, I think this is a good one, especially if you've already got some background and you're looking for some general content to build upon it using academic books.

Overall, I learned some new stuff in this course. The programming exercises were interesting. However, the instructors really should provide more explanations regarding the calculations, formulas, and exercises' instructions and feedbacks. In many occasions, I had to looked up explanations online to solve the homework.

The videos are super nice and professors are excellent at explaining the concepts. However there's a big difficulty gap between the lectures and the assignments and little to no feedback when you have difficulties solving the problems. Plus, the programming assignments are just not covered in the lectures.

Very challenging and interesting. However, the last module was a bit confussing and needed to look for materials on the Internet to really grasp a bit of understanding on the subject. Although sometimes frustrating, I think it is a good start to recap mathematics with a very practical approach.

Overall, it was a good summary to understand linear algebra. To get into the topic, I had to read through additional material as the videos and tasks provided in this course were a little shallow to my liking. I, personally would have liked more applicable machine learning examples.

I believe that the programming required in the assignments are not beginner level. I had never coded on Python before and I thought that there wasn't enough support on how to test my code before submitting, for example. On the other hand, the math topics were really interesting.

I give this a three because the course focuses on themes with a macro lens while not giving the microdetails much explanation. Good foundation and interesting topic, but it seems counterintuitive for me to have to supplement the lectures with youtube lectures...

I felt that the course does not provide any concrete examples of solving for scalar and vector projections and we're left with searching online for those which makes the course at least in the areas of scalar and vector projection confusing.

It was too fast for me. I answered many questions just by chance. But i got an overview of the concepts like diagonalisation , inverse, transpose, basis, span , eigen and so on. I am hoping that i will build on this.

I searched a lot of additional information in order to understand something. This is the most disadvantage of this course. Nevertheless, the course have many exercise and labs, which were interesting.

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.