Pros:

1. Concepts are explained in simple terms and complexity builds slowly over time. I felt more confident after each video rather than confused like with other courses.

2. Each video has small quizzes to solidify your understanding of small topics on the spot

3. Solving weekly quiz questions with pen and paper helps build mechanical memory.

Things that could be improved:

1. It would be nice to have a pdf file with all formulas in one place to refer to.

2. I wish there were a bit more examples of eigenvalues and eigenvectors, I had to do external research to fully understand the topic.

Overall, great course for beginners and those who have already started learning ML and want to get better intuition of math behind it.

Overall, I think this was a good introduction to linear algebra. It was fairly easy to follow along and complete assignments. I learned a bunch and got some new perspective on my overall journey as data scientist. If you want to be successful with this, I would recommend actually doing some extra practice manipulating matrices, playing with the different dynamic graphs this course offers to get some more intuition with how things move and possibly doing some supplemental learning watching youtube videos on anything that gave you trouble or checking out the same material on Khan Academy (recommendations they give as well).

Things became really confusing to follow around the eigenvalues and eigenvectors. I had to rewatch the videos a handful of times to try to piece together what was happening and how. There was a couple "Tada!" moments and I wasn't sure how we'd gotten where we were. When it came time to do the quiz, I had a couple questions I had to guess since I couldn't make the connection from what I was lectured on vs. how to complete problems I was seeing.

To be fair, I could have leveraged the forum more to ask questions and gain clarity but you have to go to a different website, register another account, scroll past the first page of announcements and general discussion, reverse the order of the thing you're looking for to find the math specialization header, click into that to find the M4ML Course 1 (not the Linear Algebra course that my brain is looking for) and then the specific week you're in. As a Coursera user, there are some basic things in certain places that I expect to find them. I'm sure there are reasons for everything but this course lacks a key integration for a classroom when you have to go on a small journey that requires more personal information be doled out just to have a conversation with your peers and teachers.

everything was very great and wonderful for the material except for the eigenvalues and eigenvectors, it feels so off and lacking in details, luckily i'm an engineering student who had studied about linear algebra before so i was able to follow trough. well if you are a new comer for this field, i think you should prepare more for the eigenvalues and eigenvector materials

Too basic and chaotic

It breaks my heart to only give this course two stars, but some things need improvement. The videos are masterfully explained and I loved every second, but the python assignments are very buggy, especially the one from week 3.

I believe that the main problem with these assignments is that they are a bunch of code that you can't understand absolutely anything. I strongly recommend that deeplearning update this codes with comments on each line so that we can understand step by step what is being done. Also, I went through several situations where the value was correct, the cell test was green, but the grader gave me 0/100.

The errors menssage from the grader do not explain the reason for the error at all. Finally, the deeplearning forum is dead and the administrators either ignore or just don't bother to answering the posts.

Too simple

This course is good for you if you are beginner in mathematics knowledge in Machine Learning. Special feature that I like from this course is you will learn how to integrate mathematics formula into python programming language easily. I am not really good in python language but their instruction in their programming assignment is easy to understand and guide me step by step that I can finish all of their assignment. Furthermore, the community feature is also helpful so that I can finish my programming assignment as well.

Overall, when I take this course, I don't only learn mathematics in Machine Learning but also python programming which is beneficial and unique experience for me.

Great course and Great instructor. Happy to be enrolled

This is too light.

They teach banana and they ask to solve assignment with python

Do not waste your time

They already wasted my time for 2 weeks

So bad course

Two thumbs up! Linear Algebra for Machine Learning and Data Science by DeepLearning.AI definitely makes for an excellent beginner's course for appreciating the significance of Linear Algebra in ML and DS. Kudos!

Best math refresher course. I like this style of the course structure. Starting from basic and intuitive examples, but following the steps I realized I understand all-important concept

I enjoyed this course very much. Every concept was well introduced and explained with examples. It's the best for beginners who want to algebra for Machine Learning.

Very helpful and initiative course for who has a good mathematics background or not.

Excellent course, It starts from zero and explains really complex concepts!

Great job!

It is a great course that explains complex concepts in an easy and fun way. It is a great math refresher for those who want to learn ML. However, it feels like the last week of the course is too narrow and doesn't cover all the things you'd need to successfully pass the final quiz. For example I don't recall covering the relation between rank of a matrix and number of eigenvalues (and eigenvectors). The difference between eigenvector and eigenbasis. And how to determine the correct eigenbases if there is two of the same eigenvalues. I had to google it all on my own and I'm still not sure about the last one. Hope the community helps me.

Instructor has a great combination of subject knowledge and ability to explain topics in simplified manners. Each weeks topics were introduced in intuitive manner with good visualizations before diving into math. The visualizations in videos and quizzes were very useful. NumPy notebooks were structured in logical manner

Couple of suggestions for improvement - It would be useful to make slides for course available. Also the last week's core topics on eigenvalues and eigenvectors had very minimal video coverage and seemed rushed

I did learn from this course but I feel there's a big room for improvement.

Finding the community/discussion page for this course was confusing and it took more clicks than it should.

Quizzes were introduced a lot in the beginning but I felt they were lacking in the middle weeks.

My most important feedback is that I feel the course did not adequately explain the applications of the lessons we were learning. We need deeper explanations/comparisons/illustrations showing how the mathematical concepts are applied in ML, I know there are labs but I felt they weren't enough.

Instructors aren't replying to many of the questions posted in the forums which their answers do in fact matter.

The answers to the quiz questions are sometimes not explained, you do have it 90% of the time but sometimes it just shows "Correct!" which does not show the work done for the answer. , also linking each question to the lectures would much appreciated (I know sometimes its there but not always.).

Something I would love to see is how about some math exercises for each concept explained? That would greatly cement the ideas taught to us.

Video explanations are clear, but too simplistic even for the graded programming activities. Also, it is not completely clear the relationship between the linear algebra operations and the actual AI applications. From this course I remembered knowledge that I learned 20 years ago while studying Systems Engineering - linear regressions as a form of AI, taking advantage of parallel computing was interesting. However, evaluating basic procedures as computing a matrix determinant by hand and other operations, are not relevant to the direct relationship with AI and DS. It was a little disappointing to be honest.

It is a great introductory course to linear algebra, but it misses some fundamental steps to better understand and use the tools taught. Labs are guided but the naming does not match that of the course. Some more exercises and examples should be provided for each lesson. Integrated with some of 3b1b's outstanding courses it will give you a great understanding of the subject and the ability to take one more step in becoming an ML engineer.

A pure and concise intuition builder! I am on the younger side (14 years of age) and I adapt to concepts REALLY well if given the right direction. Not only this course but specialization is the pure definition of that!

Excellent course, yet some fundamental knowledge needs to be detailly described. You'd need to read more Linear Algebra books to master those skills.

It is the most interesting algebra course I ever took. The apple and banana system is really clear and simple to understand vector transform.

Awesome content and delivery! The visual illustrations and flow of the topics build very well on each other towards reinforcing intuition.