Great course and Great instructor. Happy to be enrolled

Great job!

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.

Waste of time. Can be used only as a refresher course

Easy of follow, lot of examples while following the course a very good additional material for preparing or improve the understanding of each topic at the end was a good investment.

Increíble la forma de explicar, te lleva de conceptos muy sencillos a entender los más complejos intuitivamente. Un curso perfecto en todos los sentidos, el material es brillante.

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

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

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

The course structure is very good. For someone using it as a refresher as I have been it felt comfortable.

The only drawback I felt is that some assignments in the programming sections are not clearly defined about the output expected or process to be followed and got a couple of unexpected errors.

I missed some important concepts like Vector Projection and Matrix Calculus, just to exemplify some. I hope the other courses cover them.

one of the worst courses i ever took, maybe the worst. my problems in this course are the following: 1- the course material doesnt teach the required skills : as a person who started studying AI from a year, i felt the need to learn math deeply so i can understand more concepts in AI better, but this course doesn't even provide close to enaugh knwldge in linear algebra for AI, it's too short and the concepts are the basics 2-the lectures teach by example not by rule : here i mean that instead of teaching you the rules required and show you some examples on applying this rule, no , it just gives u approximation on the rule in yoour head by showing you 2-5 simple examples, and no enaugh generalization is mentioned or proof of any rule (m telling you most of the cases he doesn't even state the rule of a concept) 3-the code part is good for theory but terrible for application : unlike the videos, the markdowns of the code labs had good short paragraphs that explain the concept with it's rule, but the coding part was bad, i feel that i don't need to code the concept to confirm that i got it , and it's too long and easy 4-the quizes are either easy or impossible : cause the material of the video wasn't enaugh, some quizes are totally impossible to solve just by watching the videos unless you're a genuis and you could get some concepts that are derived and far from the concept in the video, specially in week 4, otherwise the quiz is easier much more than it should , graded quizes are quite good tho.

I think it is a little bit too much python for a math specialization, I also was expecting a better comprehensive math. You also cant teach python with only 2 notebook and then want them to solve an algorithm implementing python. If you do that you have to make it as a prerequisite or provide a deeper python course. i cant see a prerequisite for the specialization. It is really weird saying never mind the ML side and put a coding assignment end of every week without even telling it. I was really expecting a comprehensive math course but it is really disappointment course for me. By the way 4.5 stars is really questionable for me. it is most likely because of Deep learning.ai effect. If i would have prepared it, it would be most likely 2.1 stars or something.

The course is too difficult for a beginner, it's very fast paced for my liking.

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!

Very visual and application oriented and gives the context for machine learning and where linAL is applied in PCA and neural networks. The structure is really byte sized and fun to work with.

This course is truly exceptional for individuals eager to strengthen their grasp of Linear Algebra concepts, paving the way for a deeper understanding of machine learning and data science.

While people focus on teaching how to solve problems basically, It is very good to see people speak about maths like science as a concept with good visualization!. Great work guys.

The course and instructor were awesome. Added to that the course materials have been provided without any problem.

Graphic and simple approach that helps to understand fundamental concepts that are often not easy to understand

Great course. Looking forward to the next chapter in this specialisation.

I now understand the mathematics underlying machine learning.

You have a clear expression Luis ... We appreciate you