First, I'm a non-native English speaker, the language barrier is really a tough challenge for me to learn this course. However, I think this course is created for international learners, so these problems should be solved: - Luis's teaching is good. But I found there are some missing information or formula needed to solve the exercise. I have to Google a lot, which mean the course is not covered well.

- Exercises are really confuse sometimes. And it's not only me, I checked and many student got confused problems on Community.

- I would love to have more illustration. Some visual effect to point out which part Luis is talking about in the presentation is also good to have. Once again English is not my primary language, and Math is a lot of numbers, symbols and a lot of terminologies. I'm pretty sure even native ones may get lost too.

- I wish there is a Discord server for students, because it's usually quicker to get response there. The community page's UI/UX and navigation is not good. - Put the Notation on top of 4 modules. Why putting it at the end? I wish I knew it earlier.

1- Few topics I could not understand like they asked a 3X3 matrix question in the assignment but it was not discussed 2- Some assignments don't have clear instructions. Example Week 4: Question 7. 3- QnA is fine but its a bit delayed like we ask question on stack overflow and someone will answer when they see it. I think given the amount of money we are investing in this course there should be dedicated live QnA sessions. Then I can go for a 4 star rating. If i am supposed to just see videos why cant i just see it on youtube. 4- My certificate says Coursera learner instead of my name. I was expecting that to be my name.

The course was a success in that I have much more intuition about linear algebra now and I was able to get farther in this course than my previous attempts to learn linear algebra. Things I didn't like: the increase in difficulty in week 4 was a bit ridiculous, all of the programming assignments were "fill in the blanks" whereas I was hoping to build some stuff from scratch, and it's not clear to me how some of the later course material is applied in ML.

The course was solid right up until the last week, when the material on eigenvalues and eigenvectors started to feel very rushed and poorly explained. Some questions on the quiz for this section were extremely hard to interpret as a result. Worst of all, the course did a poor job of explaining why eigenvalues and eigenvectors are important for machine learning. Aside from these shortcomings, the course deserves at least 4 stars.

Visual examples are nice, but this is maths and IMO the course lack of some match way to be added.

Also, some parts of the labs problems, for graduation or not, have ambiguous parts some more words or examples can help.

I suggest take a pure linear algebra course in place of this, and after going for a machine learning course.

It's a decent course but extremely easy. The labs are merely fill in the blanks with basic programming. Hardly covers any machine learning algorithms, a basic neural net and PCA are all you will encounter. It is still a nice refresher if you need to review basic Linear Algebra concepts.

The programming assignemnts are very poor, the topics are simple but at the same time not explained in depth enough, especially week 4 is extremly chaotic and makes no sense. also how this is actually used in the real world of machine learning is extremly poorly presented

The course assumed a lot of prior knowledge or perfect understanding; I found myself looking to Khan Academy for deeper explanations of many of the topics. Either I don't belong here, or the final programming assignment was absurd.

the course has good features but I think it lacks basic theory if you are not familiar already with linear algebra concepts. If that's the case I recommend better jon krohn free algebra courses instead of this

Not enough explanation when it came to eigenvectors/values/spaces. The first 3 weeks were very explanatory but for this being a beginner course I think longer videos with more explanation would be better.

The videos are easy to understand and really good. But I didn't like the graded assignments, they were hard to understand and sometimes there wasn't enough explanation.

In part brilliant, in part very bad. Some of the videos are too short and programming assignments are sometimes hard. A few videos on python and numpy will help.

While it clarified a lot of things, some parts were not well explained. If I didn't have STEM background and it wasn't a refresher for me I would struggle.

To be honest the videos are too plain and boring too watch I think the videos should be more enjoyable and give a more understandable examples

Gives a very good understanding right uptil week 4, but then Eigenvalues and Eigenbasis could be better explained.

Very basic fundamentals could have done more. But overall a good course to gain insight into the topic

eigen vector and eigen values part is not explained briefly other than that course is just amazing.

Very basic, some interesting topics are omitted. But visualizations are good.

Eigen Values and Eigen Vectors wasn't explained in great details

the grading criteria is not accurate, slides are not so clear

too easy

I had to fix so many issues with indentation, a broken jupyter notebook, broken test files. Don't waste your time. And to force people into PAYING for a certification earned DURING A & DAY TRIAL is just shitty man. Seriously, go to hell. Why would I pay for a course that constantly broke, and spent 3 hours fixing? That's so beyond greedy and money ahead of education that it isn't even funny. Assholes.

This course was not helpful to me. I didn't really understand what I was achieving in the last lab and although I passed all the quizzes, I found myself going to external resources constantly for answers. It's this weird mix of overly simplified metaphors and then slides that truncate the mathematical steps they show which left me at least having to pause it and figure out what happened. IDK.

The intuition (for ML & DS) was not established; the explanations were often thin; maybe drop the "for ML and DS" part off the description?

Not possible without knowing Python 3 already. Also doesn't really talk about ML so I don't know how any of this is applicable to ML.