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Back to Mathematics for Machine Learning: Linear Algebra

Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London

4.7
stars
10,786 ratings
2,149 reviews

About the Course

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning....

Top reviews

CS

Mar 31, 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

HE

Aug 8, 2021

the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering

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1626 - 1650 of 2,160 Reviews for Mathematics for Machine Learning: Linear Algebra

By Kenneth B

Jan 9, 2021

This course provides a good overview of the basics of linear algebra for machine learning tasks. I recommend the class and I'm glad I took it, but it could be improved by adding more thorough explanations for quiz answers. This would have let me better understand how to arrive at the solution to a problem.

I didn't have any Python experience coming into the class and made it through OK, but there were times where this lack of knowledge interfered with my ability to complete the Python labs (particularly the last one).

By Osaama S

Sep 29, 2019

Instructors have done a really good job at introducing the fundamentals specially from a graphical point of view which allows you to build your grasp strongly around the topics in a way that is not accomplished in a traditional college classroom. However, I would say perhaps there could be more challenging questions on the real world applications of linear algebra in machine learning followed by in-depth step-by-step solutions in order to really get the application-based learning inside your meat.

By Gabriel L S

Aug 12, 2019

I like the the structure of explaining the theory using examples (in this case, geometric/visual examples). However, I would love to have further understanding on the basic linear algebra topics (or at least be linked to websites that explain this further) to allow flexibility to students like me who has zero knowledge on linear operations. Overall, I was able to overcome the challenged through self learning, understand the concepts well, and appreciate the applications in machine learning.

By Allan d Q

Nov 13, 2020

I'll go through this one once again, I feel like I'm not fully comfortable with the content. I had to use loads of external resources in order to pass through the quizzes and some of them I guessed, I must say.

The coding ones were a bit easier since I'm a software engineer already but yeah, I definitely need to through it again but overall, I've discovered many new things that were unknown unknowns.

Thank you all for putting this kind of content online for everyone! Outstanding job!

By Yuriy G

Jan 31, 2019

Thanks a lot for this course! The explanations and lecturer work were brilliant!

It's very good for introduction but it lacks a strict wording, definitions and some generalizations. Especially in the section of changing basis. When after considering a number of examples, I really want to move on to the general case.

Anyway, you've a made a great work because anyone without any preparation can get acquainted with very deep mathematical ideas.

By Kumar S

Apr 14, 2020

This is a very good course and helped me a lot in getting started with going through mathematical concepts of machine learning. It has taught me lot oh Linear algebra stuffs in a very intuitive way. However the quality of assignments could have been better and some of the concepts that were important and needed more explanation was skimmed through rather quickly. However, I am really satisfied with the overall learning that i got.

By Nikita V M

Feb 6, 2020

Excellent instructors and video quality. Some frustrating elements with assignments being either somewhat unclear or redundant. The only severe flaw was that grader feedback was entirely pointless, as it made no effort to even give an example of what went wrong. Simply saying that something was incorrect without providing, say, an example of the input matrix that failed, in no way helped advance my understanding of the problem.

By Leandro C F

Mar 4, 2021

This is one of the best math courses I've took in Coursera.

20 years ago, I took linear algebra in my University. And now I needed to remember some of these topics. For this purpose, it is excellent. The way matrix transformations is addressed was new to me and make the topics very easy to understand.

Unfortunately, the topics of week 5 (eigenvectors and eigenvalues) were not so well taught as the topics of weeks 1 to 4.

By Alex R

Mar 28, 2021

An excellent visuals of application of Linear Algebra. Some of the homework is not based on the material presented in the class, and requires you to go to external sources to learn more. When doing homework, sometime, it unclear what additional material you don't know and need to learn outside of the course work, to solve the homework. Other than homework, it's an great class to take! I've really enjoyed this class!

By Khubaib A

Jul 27, 2020

Python. Python is needed. It'll be very hard to progress without that. Otherwise, the course is great. Instructors are good. Week 5 is sort of a stretch. The Page Rank Algorithm is not really explained well (some say that it is not explained completely) and dedicating an entire week to just eigenthings does not make a lot of sense. The exercises are good though. A good introduction to the basics of Linear Algebra.

By William L

May 19, 2018

Video lectures are great and really help with understanding why you are learning the material and what the concepts mean. The programming assignments and quizzes are challenging. There were some cases where I did not understand the quiz question and did not know why I got it wrong or even correct if I guessed. Access to solutions after completing a graded assignment or even after the course would be beneficial.

By James G

Apr 1, 2018

It's quite good. The material implies they are aiming to teach linear algebra and basic Python programming basically from scratch, but it goes over topics so quickly and skims over so many details that I suspect this course only works if you've studied much of it before. Even though I have studied much of this before I still had to go and find other sources of information, as the explanations here are so brief.

By Kitty

Jun 12, 2019

Generally great course. Explanations are very clear. Cons: ① no textbook/slides/reading materials etc., have to take notes and screenshots for every single thing you want to record. ② The content is not enough. Way too less knowledge covered than college-level linear algebra course. I took this course to refresh my knowledge and it turned out that more than half of the contents are the ones I still remember.

By Mohamed H

Mar 16, 2021

The course did a good job in building up intuition about linear transformations and change of basis. It's a useful review of the core of linear algebra and can be implemented in many fields. The programming assignments were very basic, so maybe a bit more challenging ones will be better. I didn't like the last module of the course about Eigen analysis. While the topic is very useful, it was a bit rushed.

By Amit A

Aug 30, 2019

Eigenvalues and eigenvectors while explained conceptually very well, the jump to page rank and transformations using them was bit hand wavy. May be it is not that important or the topic is too complex. I think I have to go through it multiple times to get the gist of it once again. I might if there is real applciation of it in ML.

The course is still very good and thank you for sharing it with us.

By Grant T

Jun 30, 2020

Without having taken a Linear Algebra course previously, I thought the course was worthwhile to introduce topics. However, I had to spend ample time researching outside of the course IOT learn. In addition, although I was introduced to many LA topics, I still need additional practice in grasping certain concepts. This course is a good introduction to topics you'll have to research on your own.

By Lizzie M

May 25, 2020

It's a really good course with great tutors, really engaging and easy to follow. It can be very challenging at times if you don't come from a maths background. There are some assignments which are much harder than the examples in the lectures so some extra material to help you with those assignments would be great, otherwise personally they demotivated me. Other than that the course was good.

By Rob O

Apr 10, 2020

Having last taken linear algebra many years ago this course was a welcome refresher. Overall the course is excellent with clear explanations, good examples, and opportunities to practice your newly learned skills. That said the last assessment on eigenvectors did not evaluate learning and skills as much as reasoning about special cases and focused on problems better solved computationally.

By Oriol C

Jun 6, 2021

The course is good as a refresher and it helps learning the intuition behind some concepts. The professors are very likable and do their best to explain the materials. However it is not good if you are looking for rigorous explanation of the mathematics as they go very quick on most of the concepts.

Taken this into account I would still recommend it to get some quick grasp on the intuitions.

By Mackie Z

Aug 3, 2020

I have experience with python numpy and pandas, so I found the assignments reasonable, but i think the programming assignments can be confusing to someone who has never coded before. There're a few times when I found myself lost in the instructor's explanation and needed to find more clarification online, but overall the videos are of great quality. It's a great course for self learning!

By Pavel S

Dec 12, 2019

The biggest problem of this course is that dot-products are introduced before linear transfomations. I understood dot products through 3blue1brown videos and they are more intuitively explained as the product of the lengths of the projection and the vector projected onto. It is a subset of linear transformation a matrix vector multiplication where one of vectors is transposed.

By Alex H

Jun 8, 2020

I'm sad, because I finished the course, but instead of a solid understanding of linear algebra, I mostly feel confusion and frustration from what I was not able to ask the professors. But overall, the instructional videos are high quality, and the quizzes were challenging. At least it forced me to think critically about the subject, so I don't think it was a waste of my time.

By Fang Z

Jun 11, 2019

The course generally is good. However I think there are some problems in this course: 1. The course pace is too fast, some concepts are hard to understand with few minutes lecture 2. The after-practice didn't help me to boost my understanding to the lecture. Even after I finished the practice, I still wonder why this happens 3. The final quiz has too much calculations.

By Prasad N R

Sep 30, 2019

I was expecting a lot from the course. But, it covers only the very basic portions. For example, I am not sure if I can start understanding the difficulties with normal equations and portions of linear algebra based on calculus. Also, it does not discuss parallelism of ML with linear algebra. I am not sure if this will help me read and understand Andrew Ng's ML papers.

By Musiboyina Y

May 26, 2018

The course content was spot-on, covering some of the most important basics for math in machine learning. I wish there were more programming exercise based assignments and less hand-calculation based quizzes to make it close to real world applications. Overall, loved this course and highly recommend it to data science enthusiasts taking baby steps towards deep learning.