Вернуться к Mathematics for Machine Learning: Linear Algebra

# Отзывы учащихся о курсе Mathematics for Machine Learning: Linear Algebra от партнера Имперский колледж Лондона

4.7
звезд
Оценки: 9,678
Рецензии: 1,949

## О курсе

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....

## Лучшие рецензии

EC
9 сент. 2019 г.

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.

PL
25 авг. 2018 г.

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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## 1826–1850 из 1,939 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: POR M H

1 февр. 2020 г.

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.

автор: Santiago R R

20 июня 2020 г.

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)

автор: Rong D

30 авг. 2018 г.

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.

автор: TirupathiRao p

16 мая 2020 г.

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.

автор: David D

18 авг. 2020 г.

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!!!

автор: Aurel N

8 мая 2020 г.

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

автор: Akeel A

22 июля 2020 г.

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.

автор: Manuel M

25 янв. 2019 г.

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.

автор: itwipsy17

25 февр. 2020 г.

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.

автор: vignesh n

12 сент. 2018 г.

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.

автор: Alexander D

7 авг. 2018 г.

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.

автор: Santiago M

14 сент. 2020 г.

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.

автор: Cindy X

20 дек. 2018 г.

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.

автор: Christos G

24 янв. 2021 г.

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

автор: Atish B

24 сент. 2020 г.

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.

автор: Serdar D

15 февр. 2021 г.

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

автор: Amal J

15 июля 2020 г.

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

автор: Jorge G

14 авг. 2020 г.

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.

29 дек. 2019 г.

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

автор: Aaron H

17 окт. 2019 г.

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

автор: Indira P

7 мар. 2021 г.

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

автор: Kate G

19 нояб. 2020 г.

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

автор: Matt P

24 февр. 2019 г.

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

автор: N s n r

11 дек. 2019 г.

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