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

10,858 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.


22. Dez. 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

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2051 - 2075 von 2,179 Bewertungen für Mathematics for Machine Learning: Linear Algebra

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.

von Amal J

15. Juli 2020

The course gives a good beginner-friendly Introduction to Linear Algebra. But the courses could cover a little more topics in LA.

von Jorge G

14. Aug. 2020

I would give it 3.7, examples are good but the vectors the lecturer draw were no easy to understand because of drawing by hand.

von Nicholas G

21. Juni 2022

TEverything is good until you reach the coding assignement. Then it is a complete disconnect with no resources available.

von Badri T

29. Dez. 2019

The Eigen system could have been better explained. The last quiz was too hard and the concepts required were not covered

von Aaron H

17. Okt. 2019

Lot of the concepts seemed glossed over and could have used more guided practice and/or linkages to real world problems.

von Indira P

7. März 2021

It is so complex and contains so much knowledge but hard to understand for beginner or intermediate in mathematic

von Kate G

19. Nov. 2020

The instructor is skipping a lot of material and the quizzes require working with external sources to be solved.

von Matt P

24. Feb. 2019

This course would be perfect if more elaboration on the maths required to complete the quizzes, was provided.

von N s n r

11. Dez. 2019

i expected a practical mathematic approach rather than only mathematical approach.but page rank algo is good