Good Explanation for Basic Linear Algebra concepts

Loved it. So many old memories refreshed

it was an amazing course.

thanks

A great great Course!

So interesting.

Perfect!

good

The coding project could use a revamp and information in them can be moved to the lectures. Also, the slides need to follow the lectures with bullet points for the key point. Please fix the slides. The instruction is great and so are the responses. I would recommned this course; However, there are major areas that need improvement.

This course was great! It provided me with a deeper understanding of several topics. I highly recommend it. However, I would have appreciated learning about matrix factorization and the application of linear algebra to linear regression as well, which was missing in this course.

Lectures were excellent but some of the labs and and quizzes contained concepts not covered in the lectures.

Eigen Vectors/Values and Transformations could have been explained better

Excellent intro to the fascinating world of Linear Algebra.

Although the course helped me stimulate interest in the right direction and helped me narrow down the context (from all the distractions online), yet this course did not help me construct a clearer intuition so needed to venture all on my own. Its almost like when a baby all of a sudden finds a new skill/trick/ability and amazes her/himself - at that very moment something happens deep in the brain that taunts the baby to explore further and deeper to hone that skill completely on her/his own - I miss that intuition from this course. Of course, on the other hand, I understand this is the best what you get for this price and for everything else you need to be Harvard educated.

I think a little more of a level would be great, it is always good to refresh basic concepts but the level for this course is maths reaaaally basics.

In my humble opinion, it looks like data scientists, deep learning researchers, machine learning engineers, and so on... do not really need to know maths to do an actual good job while it is totally the opposite the more maths you now the easier for you to understand when something is wrong.

Note here: maybe statistics for data science would be a great new course, and there you can start from the basics since statistics are typically pretty badly taught at schools.

Need more tough problems in assignments to build deeper understanding. Visual explanations for linear transformations could have been better. Explanation of span and bases could have been better.

I feel I've learnt a decent amount of Linear Algebra from the course. Stars reduced as I had to turn to supplementary learning tools at times, and I would have preferred more practice labs.

Good course however too shallow.

I would like to give the instructor five stars for his basic instruction, but I have to give the class one star for the lack of support around any of the coding exercises, and so I've averaged it to two (2.5 if I could). Professor Serrano does a nice job of providing a basic understanding/intuition of Linear Algebra, but the course needs A LOT more supporting material to help practice and develop a deeper understanding. As for the coding exercises, which are necessary for a certification of completion, there is next to zero supporting materials to help build a competency necessary to complete the assignments. The coding assignments are very complicated, without the needed intermediate steps to help you understand the deeper material. If the coding material is needed for the certificate, it would be better if there was more supporting material/tutorials around implementing linear algebra in code, but instead it goes from 0-60mph immediately. There are better resources out there for learning this material.

Section on Eigenvalues is terrible. Makes zero sense. You never once even mention the word "eigenbasis" in the curiculum and now it's on the quiz. "contrust the eigenbasis". WTF is "contrust"? What am I paying you for if not to explain these things. "Google it." is a lazy answer. I'm taking these courses so I can build an AI education platform that makes you obsolete.

I am unable to find the course forums or any way to interact with the course instructors or other students. The "Discussion Forums" link in the left-side menu takes me to the Forums page which has three tabs: Posts for you, All forums, and Your activity. The "Posts for you" and "Your activity" tabs show "No posts yet" and are otherwise blank. The "All forums" tab is completely blank. There are no links to create a new post. Also, I received an email this morning entitled "How is your course experience so far?" that suggests posting in the "Discourse community," but there is no hyperlink to this community and I'm unable to find it. Lastly, there is a "Discuss" link above each course video which, when clicked, takes you to a page that reads "Oops! Looks like this thread isn't viewable or does not exist." Perhaps Coursera is experiencing technical trouble at the moment, but that still doesn't explain why the email doesn't link to the Discourse community.

Sin ninguna duda aprenderás más en youtube, que aquí. Lo unico que puedo decir es que cada día que pasa me arrepiento de haber tomado este curso, pensé que sería distinto. Los laboratorios son lo peor, solo es texto y nada más. Incluso puedo decir que existen mejores cursos que te explican el área matemática junto con la programación a la par, en cambio aquí la toman por separado, deficiente... No lo recomiendo por ninguna manera. Mejor anda a youtube y encontraras mejores cosas

The course is too basic and too shallow. Usually, I am thinking that explaining things in a simpler way is better. Though the course completely ignores academic abstractions therefore I cannot recommend it for people without math background. As a refresher was not that bad.

I am grateful to complete "Linear Algebra for Machine Learning and Data Science" course with 100% grade. This course provides all the concepts which is required for machine learning and data science. It is a course that focuses more on the intuition part rather than just formulas. With the help of this course, I understood linear algebra in a completely different way than I did during my graduation. I highly recommend everyone who has an interest in data science to enroll in this course. I would like to express my gratitude to Andrew Ng and Luis Serrano for providing this wonderful course.The course covers a wide range of topics, including: System of linear equations, singularity, determinants, rank, linear dependency, row echelon form, vectors, dot product, orthogonality, linear transformation, eigenvalues, and eigenvectors etc.Additionally, the course covers the "linalg" module from Numpy Python library, which is commonly used for linear algebra computations. It also includes programming assignments that allow students to apply the concepts learned in practical coding exercises.

It has been intriguing to me how Linear Algebra (non-singular systems, linear transformations, vectors, matrices — dot products, inverse, determinants, eigenvalues, and eigenvectors) plays a crucial role in understanding and developing machine learning (ML) algorithms. And how Linear Algebra provides a framework for representing and manipulating data, as well as the mathematical foundations for many ML techniques. 

Thanks to Prof. Luis Serrano and the Deep Learning.AI team for making the course so easy to understand with a common sense approach. Sure, it has satiated the hunger of Business Analytics enthusiasts like me. 5 out of 5 for the course and the impeccable delivery. It has sparked an urge in me to learn and practice more, to transfer the knowledge to my students, and to discuss with like-minded people.

P.S: Thanks to Lucas Coutinho for patiently attending to my queries and guiding me along wrt the Lab exercises.

I found the Linear Algebra for Machine Learning and Data Science course to be an excellent resource for improving my understanding of linear algebra. The course covers a wide range of topics, including matrices, vectors, and eigenvalues, and the material is presented in a clear and concise manner.

One of the only downsides of the course was the lab sections, which were challenging to follow without any accompanying lectures. However, with enough time and effort, I was able to work through the labs and gain a deeper understanding of the course material.

Overall, I highly recommend this course to anyone looking to improve their understanding of linear algebra for machine learning and data science. Thank you to the course creators for putting together such an excellent resource.