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Bewertung und Feedback des Lernenden für Mathematics for Machine Learning: Linear Algebra von Imperial College London

11,368 Bewertungen

Über den Kurs

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....



9. Sep. 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.


25. Aug. 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

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1726 - 1750 von 2,247 Bewertungen für Mathematics for Machine Learning: Linear Algebra

von Emmanuel G

14. Apr. 2021

Covers some good basics, but I feel that I would have struggled with the programming assignments if I didn't already have some practical experience with data science in Python and linear algebra. In particular, the last 20% of the course felt (eigensystems) felt rushed and could have been expanded upon a bit more thoroughly.

von Vinayak N

14. Okt. 2018

Good for starters. It gives a holistic view of linear Algebra. Geometric interpretation of Eigen Vectors was the highlight of the course for me as I wasn't aware of it before and the instructor helped me understand the concept very well! Thanks for putting forth this course and hope to see more in the forthcoming sessions :)

von Rick M

21. Juli 2019

Overall, I thought this course was worth the time. Some of the material was challenging, but the instructors were pretty good at explaining clearly. Just a head's up: there is relatively little reading material here, so if you struggle to learn through videos you might have a hard time. That part was a challenge for me.

von Henri S

9. Okt. 2020

Could be nice to have the complete mathematical definitions given in an annex for those that are interested in refreshing their maths more than understanding the concepts broadly throughout the examples. Otherwise very well taught, I like that there are many examples where you have to get back to basic calculations.

von Simon W

27. Juni 2020

Good course overall and I enjoyed the top-down approach in instruction, which helped me understand the big picture before proceeding to do specific linear algebra computations. However, I wish there were more lecture contents and exercises to help me build a better foundation and clear up occasional confusions.


6. Juni 2020

The course already cover all some of essential topics in linear algebra is is a good course to refresh linear algebra and get hands on coding on how we can use linear algebra for computation. I would be great if the course also covers other essential topics such as null space, column space, pre-image, and image

von Subham K S

30. Jan. 2020

Great course!! The instructors taught in a great way with proper visualization and real-world applications.

But more examples of implementing in machine learning could have been included and a bit more concepts could have been taught.

Overall great one. Thank you coursera, Imperial college and both instructors.

von Beyza A

3. Mai 2020

I have 2 years of experience with coding. I took this course to refresh my knowledge of mathematics before I start using machine learning techniques. This course sometimes gave us the basic knowledge which helped to apply real-world situations. However, I feel like I need more exercises, basic explanations.

von Oriane N

3. Mai 2022

Very well explained with videos and a recap PDF. Guided exercices to practice with manual calculations and computer programming (Python notebooks) and questions to get the intuition of what's going on with special cases. I recommend and will continue the specialization with the other Maths for ML courses !

von Sandeep M

30. Apr. 2022

I really enjoyed the course. Great learning experience. There's one area where I felt that the course could have done better. And that is explaining the interpretations of various mathematical calculations. These interpretations were embedded in the quizzes and the assignments. But they were very cryptic.

von Luis F H

10. Dez. 2020

The videos and materials are great, departing from zero in the subject I was capable of understanding and practicing., but some programing exercises demand any knowledge in python, what makes things more difficult in a few moments. Would recommend for anyone that wants to enter into the ML world.

von Chip B

25. Mai 2019

Filled in a lot of knowledge gaps that I should have learned in high school or undergrad. I feel much more prepared for graduate studies in data science.

4 stars because the last module felt rushed. I felt that I learned more from trial and error on the quiz than from the lecture videos.

von Kara

4. Juli 2020

The content is good, and I can see that the instructors are trying to let students understand the mechanism behind the calculations. However, the lectures are too short for students to fully understand everything. I would suggest to extend the length of the videos and provide more details.

von Frank G

14. Apr. 2018

Very good class. Outstanding instructors very clearly teaching key concepts in linear algebra.

I only docked one star for two reasons:

I wish they explained in more depth how the linear algebra topics are used in machine learning.

I wish the class were a little longer and more in-depth.

von Sagar

23. Okt. 2020

Mathematics is the core of machine learning. This course is best for understanding the mathematics of machine learning. The course was in-depth and intuitive. The assignments were a bit difficult for the new programmers. But overall, the theory classes were clear and understandable.

von Sydney F

26. Juli 2019

While they explain the basic concepts of linear algebra, sometimes the programming assignments are tricky and some of the quizzes are too complicated to complete with our current knowledge. However, the course is worth taking if you want a solid math background for machine learning.

von Saurabh P

5. März 2019

the lectures were very good and on point, obviously referring the prescribed textbooks will further improve one's knowledge about the subject. i really enjoyed the programming part of the assignments, which were made to help students without any prior experience of python language.

von Md. M H

1. Nov. 2018

It would be better if it pointed out the pre-requisites of this course. Besides, the submission process of Jupyter notebook doesn't work directly. These issues need to be solved. Other than these issues, the course itself is pretty informative and the instructors are well prepared.

von Nikhil G

30. März 2018

Great course, offers a nice introductory base you can use to further your knowledge without having to take a full three month course on linear algebra, allows you to dig into some interesting stuff earlier on. Could have used a bit more feedback for quizzes and assignments though.

von Ziyi Z

26. Dez. 2020

The lectures are easy to understand. However, the quiz is slightly harder than the course material. Especially the first quiz (it gets easier in the end). The coding part requires previous knowledge of python. Otherwise, you will be so lost in the process. Overall, great course.


22. Juli 2020

The instructors were great. They explained the topics nicely. But this course should add more clarification of different topics in the video section. And it would be great if the instructors could add some programming examples in the videos. Then the course will be more helpful.

von Kevin E

27. Apr. 2020

The examples were relevant, and I could follow along with them on my own. The programming assignments helped to complete the understanding of the processes. I would've liked more examples to work through for practice, and to improve understanding. Otherwise, it was great.

von Switt K

8. Juli 2020

Nice and simple to learn. You'll get intuition of what matrices do and ways to look at a matrix. However, like the instructors say, this does not include all of linear algebra.

I do believe it provides a very good gateway to reading further topics on linear algebra though.


10. Juli 2018

The basic geometry explained by the tutor is amazing especially the dot product, determinant, etc. Although the program assignments suffices for its purpose, I would have enjoyed more if it would have been little more challenging. Overall, this course rocks on its purpose.

von Syed Z N

21. Mai 2020

The last module seemed a bit hurried. More videos could have been made regarding the topics in the last module. The video on PageRank algorithm should have more illustrative examples for allowing the students to visualize. Apart from that, this was an amazing course!