It is a great course, but I don't feel enough comfortable with my level in Linear algebra, I learned a lot about linear equations, matrices and machine learning. But I'm not sure if I could understand how machine learning algorithms works. But this course is an excellent introduction to find more information about Linear algebra.

The material is well thought out but the examples are not that great, The instructor tends to use colloquialisms which is fine but sometimes hard to follow. The examples could be more thorough and step by step. A number of times they move from one matrix to another transformation but its actually multiple steps condensed.

An excellent coverage of Linear Algebra with very good hands on assignments to make the concepts sink in. I do believe that the lectures can be improved a bit more especially for the more complex topics with more examples to work through. I had to refer other lectures and videos to really understand many of these topics.

A mostly good, thorough and intuitive introduction to linear algebra. Unfortunately, had to rely on some other resources to complete assignments in week 4 as explanations seemed to be insufficient (maybe just me). Plus, programming assignments used notations that hadn't been discussed previously.

Very strong 4/5.

I already know about Linear Algebra, but this course brings me another perspective and shows me the application of Linear Algebra to Machine Learning, a field in which I have a special interest. This course does not provide me with new concepts, but it does consolidate my knowledge and bring me tools to use it.

A very good course for any beginner, gives you a good overview of the essential concepts with profound practice. I would recommend to add more content on how a particular concept is used in ML. Eigen vector section can be improved. Overall, perfect to learn Linear Algebra for Machine Learning and Data Science.

I enjoyed the course, but felt the last lectures on eigenvalues, etc. could have been expanded on a bit. The coding lab had some good exercises, but the instructions on pagerank application exercise were confusing: it seemed like there were constraints on the values for the P matrix beyond those specified.

Pretty good overview of linear algrebra and its applicability to machine learning. However, I found the explanation to Eigenvalues and Eigenvectors to be unclear. The content could be improved by making it clearer how one progresses from finding Eigenvalues to finding and chosing Eigenvectors.

It is expected that you will be doing a lot of research or already have linear algebra knowledge and want to refresh your memory or do some programming-oriented algebra. Since I am studying linear algebra for the first time, I intend to take another course to enhance my knowledge of the subject

It is really good introductory course, refreshes all your basics of linear algebra. I had to unlearn some of prejudices in order to understand the concepts clearly. I expected a little more deep dive like Markov matrices applications, being said that it is really helpful.

دورة ممتازة لمن لديهم خلفية عن الجبر, وُضِح فيها علاقة الجبر بتعلم الآلة وكيفية تسخير مفاهيمه لها وومالهدف منها, توضيح سلس ورائع, لكن الدورة ناقصها تطبيق عملي, فلا يكفي الاكتفاء بها. لكن بشكل عام كمقدمة لاستخدامات الجبر في مجال تعلم الآلة فهي وافية ومتميزة في هذا.

Course material was interesting and most of it was easy to follow. The section on Eigenvectors and Eigenvalues could have been much better explained. The examples skipped steps, and could have been more thorough. I had to do a lot of reading on my own.

The teaching is very good, assignments and labs helped me to gain more information and practice. But, some of the lectures could have been taught better in more simple terms and with some more good examples. Overall I had enjoyed learning with this course!

Course is good, but it is not for beginner for sure as you must need prior at least intermediate level experience. If you are starting fresh and do not remember linear algebra from schooling i would suggest not to take this course before reading about it.

A good course that explains the concepts of linear algebra in an understandable manner. There were certain modules that I had some trouble understanding but pairing this course up with some research of my own and 3b1b's youtube playlist was very helpful.

The course was absolutely rewarding , Mr Serrano explained and covered it well.

But I noticed there were a few details that he forgot to mention that I went searching for on google.

Otherwise,I really liked this course and I actually learnt many things!!

The videos and theory are great, very edible for a beginner. I can not say the same for the programming modules that I found confusing. I often used other resources to understand the concepts and solve the problems. However, I recommend this course.

The only issue I has was that I am not a super experienced coder and sometimes I got stuck with simple parts in the workbook. More resources to help with the coding aspect would be nice but overall I learned a lot. Even though the struggling bits.

The course was excellent in terms of teaching, practice quizes and Assignment Quiz.

The only problem is the programming assignment where some of the application oriented concepts are not familiar to me and don't know how exactly few codes worked.

Excellent course for introduction to Linear Algebra. However, the important part which is Eigen vectors, was not very well explained, and should have had given more time in my opinion which would have developed better understanding about it

Learned the math behind Deep learning, which is essential for understanding why the algorithms work, the last assignment could have been improved a little bit, it seems kind of a rush while working on it, keeping track of all the concepts.

I like this course because of the high quality in teaching linear algebra and Numpy to solve some problem . Also I learned Matrix as well as and I'm glad to participating in Linear Algebra for Machine Learning and Data Science course .

There are some errors on prompts and notebooks that made it difficult to understand some topics (until I get that the error was not being made by me). But in general, it was an excellent experience for me.

I am grateful for this course. However, IMHO, eigenvalues and eigenvectors could be explained in a more clear way. I would consider revising the last two pieces of video and recording them again.

it's was a great experience and the explanation was easy to understand. I want to thank everyone who works on this course. but I have an suggestion that labs supported with visual content like videos