decent course. It gives a good enough background to understand the mathematics necessities of many areas of data science. could be more thorough and dive deeper into some of the content.

Good course because it shows how to understand geometrically, things that I had hitherto only understood computationally.

This is a good course, the only drawback is its failure to connect with ML

Excellent Instruction

For the most part, I enjoyed this course. Most of the math explained is fairly easy to understand. They cover the fundamentals of linear algebra, and provide plenty of assignments and practice exercises to test your knowledge. However, some of the video explanations are extremely confusing and feel rushed. For example, some videos in Week 4 and 5 like Reflecting in a plane using Gram-Schmidt and the PageRank algorithm were so hard to understand that I had to learn about them from elsewhere on the internet (I used MIT OCW a lot). This isn't very convenient, especially if you're paying for the course. Furthermore, I felt like more videos explaining the applications of linear algebra in machine learning could've been made, and the ones that were already made could've been made in more detail (for example, the term 'span' was never formally explained). Lastly, I would've loved it if there was another week dedicated solely to introduce the coding bit, because it's really difficult and takes a while if you have little or no prior experience in python. All in all though, I enjoyed this course, and I would recommend trying to complete both Linear Algebra and Multivariate Calculus in one month, because it's not worth paying more than that.

Challenging course. Much more difficult that I expected. It took me 7-9 hours a week. The overall course material itself was good building-blocks to further understand application of machine learning. However, explanation in some topics should have more detailed explanation and examples to further understand the concept. There were many times, I need to re-watch each video over and over again, paused it, and figured things out on my own. The programming assignments were the most challenging task. I just began to learn Python and found it very difficult because there were so many codes I haven't learnt before. I think for those who has not learnt Python at all may find really really difficult to pass the assignments.

I paid, started the course, felt I could not understand the concepts and discontinued. Later after a while, happened to watch 3B1B Linear Algebra videos, again paid, started the course, could able to finish. I think concepts are explained better there, with great animations.

The content was good, but a couple of times what was said didn't gel with what was being drawn/written/done. Since I'm learning, this took me longer to double check when I misunderstood something whether it was the concept or a mistake in the delivery.

The topics could be improved in the way they are presented. I always had to search for additional material.

However, the course is okay, it could be better, the forum is not that active, and some assignments are good.

many of the activities are excellent, but videos hard to follow along to at times - play them at 0.75 speed if you can. Also, the faculty is not super responsive it seems on discussion boards creating some confusion

Although the course content is good, I feel it could have been done better. I enjoyed the multivariate calculus course compared to this course.

Some exercises are completely incoherent to the preceding videos, which makes it very difficult to solve them. very frustrating

Assignment makes frustration and not able to concentrate on teaching content

I had to use Khan Academy, 3Blue1Brown, and Edward Dowling’s Mathematical Economics textbook to get through this course. Here are many points of frustration I encountered while completing this course: - Module 3's decomposition of a matrix into two vectors multiplied by [1,0] and [0,1] is unnecessarily complicated. - I had to look up terms like linear transformation and vector projection myself. This course talks about operations without providing the names of the associated key concepts, which makes systematic learning (where I build further concepts onto previous ones) impossible. - It should have been made clear that the new vector is neither the vector projection nor the scalar projection, but somewhere in between. - There is too much emphasis on physical space—not really data science at all. - The purpose of row echelon forms is not explained. - The connection between Gaussian elimination and row echelon forms is not well explained. It took me a while to realize that RREF is the identity matrix, and even longer to realize that REF is always an interim step to reach RREF. - In the example "Reflecting in a Plane," Dave never clarified whether r was a vector in our basis or the E basis, which made it super confusing when he flipped the order of transformation translation so that it was . It turns out it was a vector in our basis. But the last time we learned translation, the vector was in E and not in our basis, which meant the translated transformation was . That could have been so much less confusing. - Most confusing of all is "Matrices Changing Basis." It is not emphasized that a change of basis is, in fact, the opposite of linear transformation. Whereas we shift the vector in the latter, we do not in the former. It took me so long to realize the difference. - The determinant section is not rigorous at all. Why not explain how a determinant is found rather than arbitrarily defining it as ? - We skipped how to compute determinants and then we have to compute a bunch of determinants for the eigenvalue quiz? - Why not just use the term “adjoint matrix” in the determinant section to explain how the inverse is found? - There is not enough focus on matrix computation, which makes the eigenvector part really hard to follow. - Teaching the change of basis at the start, before matrices, is highly questionable, given that it can easily be done with matrices and that the intuition for basis requires some understanding of matrices. - Conformity is brushed through, so it’s unclear why some matrices are placed on the left while others on the right when conducting multiplication. - It is never explained why transformation under eigenbasis becomes a diagonal matrix of eigenvalues. - Matrix inversion is never taught, but then the quizzes constantly ask for it in the eigenbasis section.

1.) This is definitely not a course for beginners, especially if one does not know how to code OR if he/ she is weak in coding.

2.) As far as lectures are concerned, the faculty members/ lecturers are energetic. While some topics have been explained really well, many topics are either left without much explanation. There are some occasional mistakes on the part of faculty, which must've been edited and rectified. They have done good job in converting the lectures in to text. However, there were some mistakes in those texts too.

3.) There is no support in discussion forums from the lecturers of this course. I have seen many questions remain unanswered for many months. This is a very big drawback.

4.) There is a huge gap between what is being taught in videos and what is being asked in assignments. We can understand this by the following corollary: In the video tutorial one teacher is showing that 1 + 2 = 3. In the assignment, students are being asked to find the roots of a quadratic equation.

5.) Some questions and even their answers too technical to be understood by many students. The attempt to explain after the completion of assignment is also too technical. There should be an attempt to dive deeper to help weaker students. If time is the constraint, then make another basic course and let that be a prerequisite of this course. But please, do not mention in the introduction of this course that there is no prerequisite.

The course seemed rather lazy using classical presentation methods not going the extra mile and benefitting from more model methods of visualisation and interaction. Instead the student has to hear a lot of words and try decipher the language and sketches of the speaker. I'm a native english speaker and I had problems. Complex subjects need a language that everybody can understand - visualisation.

There was finally some interactive visuals, in the fifth and final week, but these seemed more of an after thought. Also they were not integrated into the course. They would have yielded greater benefit if the lecturer used them too and pointed out specific points. Instead this information came from a few lines of explanatory text.

Generally the course material seemed like the minimum they could get away with, almost as if coursera charges hosting space.

In conclusion, the course has been beneficial, but it could have been so much more beneficial. So next I will look for a course more tightly coupled to my learning style and requirements. If this search fails I may return.

As someone that did not really do much Algebra at school I thought this "Beginner" course would be perfect, it starts off well enough, things are explained with examples and I was able to follow OK, then it soon ramps up so fast and you are quized and tested before things are explained properly, I was spending most of my time using other learning resources to fill in the massive gaps in knowledge, things are explained less and less with fewer real examples, in my opinion this is not a beginner course and more of a refresher for people that have done this all before.

This course was pure torture. Lessons were great and interesting but then you are testied on something entirely different! You would be wise to have some knowledge of Python before starting. Be prepared to spend days trying to pass assessments that were never explained. Oh, and there's no supporting materials to help you navigate! Just thousands of pleas from other students begging for help and guidance. Took me three months to finally complete. I would have dropped by I needed it as a pre-requisite.

The course tries to cover every edge of Linear Algebra but fails to integrate each step with what relationship it has with Machine Learning. Core Formulas and Mathematical derivations are shoved down from throat without any respect for learners from non-engineering or computer science background. Other than week 1,2 rest was completely case study or example less UN-intuitive lectures of matrix formations and transformations. Needs a severe revamp with better examples and broader picture.

The content of the lectures wasn't the best. I passed, but honestly I had to learn all from another courses on YouTube. In my opinion, this course is useful only if you want to have a certificate.

Rushes through topics and themes. Will not give you a good understanding of the material. It might be meant as a refresher for people who already understand Linear Algebra.

Some concepts were not clearly explained and there were a lot of issues with assignment grading working properly.

Not good, concepts not explained clearly.

Does not explain mathematics in videos

To start with, I am already working as a data scientist and I took this course to get a deeper understanding of machine learning from math point of view. I am utterly disappointed with the course, if I knew it would be that bad, I would never have signed up. I think the major problem of the course is not only the fact that the the lecturers jump from one topic to another, and not even the fact that they start explaining the topic without clearing out WHY, but the fact that there is no support, seriously, there is 0 support and there is no way to work through the assignments solutions to undertand how some of them can actually be solved. I honestly advise people considering this course to use some exercise book instead or you can find some videos from this course on YouTube, don't waste your money and time, such poorly organised projects should not get our support