Great

thanx

Great

10/10

Keren

great

good

Good

good

nice

nice

good

good

nice

cool

good

Nice

ok

:)

ye

.

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.