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

4.7
Sterne
10,693 Bewertungen
2,125 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....

Top-Bewertungen

HE

8. Aug. 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

CS

31. März 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.

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2001 - 2025 von 2,137 Bewertungen für Mathematics for Machine Learning: Linear Algebra

von Anais P

7. Mai 2020

Very challenging and interesting. However, the last module was a bit confussing and needed to look for materials on the Internet to really grasp a bit of understanding on the subject. Although sometimes frustrating, I think it is a good start to recap mathematics with a very practical approach.

von Faye M

16. Jan. 2020

Overall, it was a good summary to understand linear algebra. To get into the topic, I had to read through additional material as the videos and tasks provided in this course were a little shallow to my liking. I, personally would have liked more applicable machine learning examples.

von Ilaria G

24. Okt. 2019

I believe that the programming required in the assignments are not beginner level. I had never coded on Python before and I thought that there wasn't enough support on how to test my code before submitting, for example. On the other hand, the math topics were really interesting.

von Thomas S

16. Okt. 2020

I give this a three because the course focuses on themes with a macro lens while not giving the microdetails much explanation. Good foundation and interesting topic, but it seems counterintuitive for me to have to supplement the lectures with youtube lectures...

von Chakravarthy R

16. Sep. 2019

It was too fast for me. I answered many questions just by chance. But i got an overview of the concepts like diagonalisation , inverse, transpose, basis, span , eigen and so on. I am hoping that i will build on this.

von G V

23. Jan. 2022

The course objectives, aims, and motives were very clear but after mid week 3, the teaching became abstract and the professors should have given little more explanation about the advanced topics.

von Meng H P

1. Feb. 2020

I am feeling like something is missing during the last part of the course when it comes to Page Rank Algorithm. There should be more explanation to how the math works or comes to its formula.

von Santiago R R

20. Juni 2020

The assignments kill this course, great instructors, and pace, in my opinion. (I am a beginner in linear algebra and I understood the concepts without needing Google or external resources)

von Rong D

30. Aug. 2018

I think the course is more suitable for those who have had comprehensive theoretical knowledge in linear algebra and intend to learn more about its practical use and its relevance to code.

von Marcus V C A

23. Mai 2021

The course is good. But the last module (week) is not so good. I think that the explanation of the Page Rank algorithm is not very good. I also think that the final test is very confuse.

von TirupathiRao p

16. Mai 2020

Overall course was good, I have learnt few new concepts which I haven't know till now. But at the end, things were not clear while putting all together for solving page rank algorithm.

von David D

18. Aug. 2020

Linear Algebra content is great, however, was not aware that a huge portion of grade is based on Python programming exercises!!! Only need to learn Linear Algebra, not programming!!!

von Aurel N

8. Mai 2020

Intuitive geometrical representations of eigenvalues and eigenvectors in 3blue1brown style. Had some concerns with a few theoretical inaccuracies of the material presented.

von Akeel A

22. Juli 2020

It was a good to review linear algebra again and see how what I learned in my first year course at university could be applied here! Plus it was good to see Python again.

von Manuel M

25. Jan. 2019

The course feels very disorganized in general. Some quizzes are about 10 standard deviations from the average difficulty, which is befuddling to say the least.

von itwipsy17

25. Feb. 2020

It is good course for machine learning. But I didn't fully understand the page rank system with damping.

More explanation of damping is needed for the newbie.

von vignesh n

12. Sep. 2018

Transition from explanation of basic to advanced concepts could have been better. There was an assumption that few things was already know to the learner.

von Alexander D

7. Aug. 2018

Not enough focus on how material connects to machine learning. A case study example would help, as would a very slow, detailed step-by-step illustration.

von Santiago M

14. Sep. 2020

Nice one. But realized I needed more foundation on this matter. So decided to abandon and level up my topic knowledge in Khan Acadamy. I will be back.

von Sanyam G

3. Apr. 2022

Good for someone who has bit background in Linear Algebra and Python. I won't recommend this work for a completely newbie as this course lacks depth.

von Cindy X

20. Dez. 2018

I think this course is a little bit hard for a beginner with python. And I hope that the teacher can talk more about the Machine learning part.

von Christos G

24. Jan. 2021

Very good explanations on difficult subjects but a bit short coverage of various cases, thus some assignments and quizzes were challenging.

von Atish B

24. Sep. 2020

Answers to Several questions in Week 5 quiz around eigen values and eigen vectors need to be revisited as they donot appear to be correct.

von Serdar D

15. Feb. 2021

This course consists of very fundamentals of linear algebra. I expected advanced linear algebra contents and more software applications.