Overall a good course - but there is room for improvement here. At the beginning of the course it was stated that high school algebra was sufficient. But I felt like too many videos were spent on solving simple linear equations - the course assumes the learner should know this. On the other hand the more complex topics like eigenvalues, eigenvectors and eigenbasis were covered in less than 10 minutes! Yes, less than 2 videos for the more challenging parts of the course. Then a fairly difficult quiz for techniques and concepts that were barely touched in the videos. People like me expect a paid course to be more self-contained. I also felt like other videos were rushed. The instructor brings up really interesting geometric properties of the dot product, cosines etc but it is just gone through way too fast, too fast to digest or appreciate it, draw other connections etc. There needs to be more connection to the ML/AI world. How is solving linear systems going to help me in day to day ML practise? If eigenvectors are relevant to PCA, then please make a video about that (the intro doesn't count). Markov matrices are brought up in the final section as a motivating example, but again, we need videos explaining this please! That stuff is interesting and we want to see applications. Now for the "positive" comments. I think Luis is a great teacher overall, and I really liked the way there were visualisations, both in videos and in other sections. Playing around with vectors you could move around was great. The quizzes were mostly good and made you think carefully and practise the concepts. The labs helped see the practical side of the concepts. The course covers good ground.

This is a beautiful course on linear algebra focused on the applications in Machine Learning. Mathematical content is at an intermediate level, not a very advanced one. Lecture videos are of short duration and each video is focused on one topic. In-video quizzes are useful to keep the learners engaged with the course material. Interactive tools are awesome for visualizing some of the theoretical concepts taught in the course. Practice quizzes and graded quizzes have been prepared with a lot of care. Application examples have been wisely selected and nicely presented with sufficient explanation. Finally, the best part of the course is the programming lab. A novice in Python programming will benefit immensely from these labs and graded programming assignments to upgrade his or her programming skills in linear algebraic problem-solving. A few suggestions to improve the course material - a rectangular system of linear equations (the number of equations & number of unknowns are unequal) could be introduced. How the rank of the augmented matrix and system (co-efficient) matrix can conclude the questions regarding the existence and uniqueness of the solution of a system of linear equations could be mentioned in the 2nd week's course material. A formal definition of linear transformation could be mentioned in the 3rd week's-course material. The mathematical foundation of PCA could have been explained in more detail.

A commendable introductory course, yet there are opportunities for improvement: 1) Augment the learning experience by incorporating additional reading materials. It would greatly enhance comprehension to have concise summaries of key lecture points. 2) Enhance clarity through more comprehensive explanations and examples, particularly in the final week. A need arose for external information to complete the last quiz, indicating a potential gap in content coverage. 3) Strengthen the educational foundation by incorporating more formal definitions and formulas into the lectures. While the basics were conveyed through examples, having initial definitions would provide a more holistic understanding. 4) Address the discrepancy in the lab work on neural networks, which seemed somewhat disconnected from the lecture content. A more detailed integration of lab work within the lecture material would enhance the overall coherence of the course.

I recently completed the Linear Algebra for Machine Learning course, and overall, I found it to be an excellent resource for understanding the fundamental concepts of linear algebra in the context of machine learning. The course provided a solid foundation and equipped me with the necessary tools to apply linear algebra techniques effectively in various machine learning tasks.

However, while the majority of the course content was explained comprehensively, I did encounter some difficulties during the assignments. Specifically, I found that certain headlines or instructions in the programming assignments were not adequately addressed in the accompanying videos. This lack of explanation made it challenging to grasp the underlying concepts required to complete those specific tasks.

The first 3 modules are great; simple and easy to follow with clear directions and decent labs and quizzes. The last module though is not very good, which is unfortunate because has some of the most important concepts in linear algebra as it relates to machine learning. The final lab is confusing and instructions for multiple parts are unclear. I had to comb through the discussion boards to figure out there were multiple bugs in the lab. I still don't understand the webpage navigation example or how it is supposed to help grasp the concepts.

It was still a good course overall - the first 3 modules were quite good, but the section on eigenvectors and eigenvalues was rushed and did not do a good job at covering fundamentals required. Really poor finish to the course.

Overall a great intro course for Linear Algrebra <> ML. My only friendly feedback is that the final video in the final week, on eigen values and vectors, was pretty rushed and was not comprehensive enough. The quizes & assignment that followed in Week 4, made me feel as if I had skipped some video(s) by mistake, given they used terminology/concepts I had not encountered before. I reviewed the videos and had taken detailed notes and I had certainly not missed anything. Given eigen values & vectors are very interesting topics, I think videos on them can be expanded. Also, solving for eigen vectors was not well explained when the equations had infinitely many solutions. In rest, the course is very good.

Very intuitive approach in understanding how a matrix incorporates information. Has a community website which is active and reviewed by TAs. Provides NN and other cosing applications which are very guided, so it only checks your theoretical understanding. Pedagogically could be a bit more fine tuned since some quiz questions take a deep dive whereas the video content doesnt (i.e. if I wasnt proficient in eigenvalue decomp from earlier studies didnt have a chance) but maybe the aim is to take the hassle and ask detailed help in QA forum. Also slides are presented but there has been additions like fundamental subspaces in slides which are not touched upon in videos.

If you already have taken a class in Linear Algebra, then this would seem like a basic refresher course. I did like how the instructor approached certain topics like systems of linear equations with simple and easy-to-understand examples. I would go to Youtube to find specific linear algebra topics that have applications to machine learning because the material covered in this course is still not enough. I thought more rigor and advanced topics were lacking in this course. Singular Value Decomposition and LU Factorizations were missing and I'd have would have loved to see them here.

I enjoyed the course immensely. The instructor did a great job of simplifying complex concepts, making them easy to understand. I learned a lot of new things, and I also gained a better understanding of some concepts that I already knew.

The only thing that I struggled with was extracting eigenvectors after finding eigenvalues. The lesson did not cover this topic, so I had to look it up in other resources.

Overall, I was very happy with the course. I would recommend it to anyone who is interested in learning more about linear algebra.

Overall the course was very informative and learned much on it but, if I had any remarks to give it would be the following and only related to Week 4: (Week 4 issues) - Course still needs to become more detailed when It came to solving matrices and equations - Eigenvalues and Eigenvectors need to be very much more detailed, also need to check more on the quadratics Programming assignment (Week 4): - Understanding shear / y-axis projections was complex - Understanding Discrete Dynamical System was quite complex

There is a beauty to this course that sometimes gets lost in the frustration of doing the labs. I have been working with python for years, and still got pretty frustrated in the challenge labs. So aside from the course, I would like the team to please add some videos or documentation for basic python, and specially, the various numpy functions. One notebook containing all the Numpy tricks and tips will be an invaluable reference!

Bad:

Horrible - Very elementary course - did not learn much..

Strange accent with very inferior & buggy transcriptions..

Volume very low..

Good:

The style of teaching was very intuitive, and the Instructor Luis seems quite creative!

Luis, put in the effort to make this better.. last quiz demanded lots of donkey work..

(How not to create a lousy course..)

The only good part is that the Author is creative & enthusiastic..!

Thank you.

Good course to refresh the basics. At some points, the intermediate questions in the videos come too quickly. In the case of eigenvalues and eigenvectors, I actually miss a bit more content or a better explanation. 2x2 matrices are nice to explain, but I would like to see further content at this point. This is even in the video on "Eigenvalues and eigenvectors", but from my point of view it comes much too late.

The course was well designed and structured. The assignment had a few bugs that I have reported in the community and hope that it will be fixed soon for new students who will be taking this course to transform their academic and professional lives in the future. And thanks to all the people who had contributed to this course and a special thank to Andrew Ng and Luis Serrano for this wonderful course.

I wanted to like this course more. Serrano is an excellent teacher, I loved the visual style, but I wanted to go deeper on this topic. I understand that it was geared for beginners; as a practicing data scientist, however, I would have benefitted from a deeper treatment. So, I hope that prof. Serrano will consider a follow up course at the intermediate or advanced level.

A good introductory course, providing the idea of how linear algebra is used in machine learning. I'd rather watch along with 3blue1brown youtube course on linear algebra to get solid understanding. Personally, would like to see a better explanation of the use eigenvectors related to the dynamic states (the last exercise) and some material on single value decomposition.

The Syllabus, learning material visualization, additional tools, labs, everthing is solid 10/10. but im having hard time to understan the way the luis talk, honestly, the way he talks is too fast for me, and the conjunction word such as "of", "and", "is", etc, are not pronounced clearly. that's why i need to watch the video repeatly and sometimes on slower speed.

Earlier I completed this course and it contained only the Mathematical concepts But now as the course is updated, it takes first Machine Learning application, introduces the mathematical concept to be used for that case and delves deeper into Mathematical concepts. I hope so that such changes are made to other remaining parts too.

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.