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: 9,591
Рецензии: 1,936

## 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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## 1851–1875 из 1,928 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: rishabh t

5 мая 2020 г.

Explainations was good but some topics was difficult to get may be due to my basics

16 нояб. 2018 г.

Some of the quizzes go beyond what is in the videos and often spent ages on them.

автор: Nicholas K

20 апр. 2018 г.

Enough gaps that I finished feeling like I really had no idea what was going on.

автор: David R M

13 июля 2020 г.

Requires an understanding of python that doesn't seem to be expressed anywhere

автор: Jose H C

19 дек. 2019 г.

I did not see any specific application of what was learned to Machine Learning

автор: Tory M

3 сент. 2020 г.

All in all this course served as a good refresher for linear algebra.

автор: Gary M F T

29 окт. 2020 г.

Esta en el idioma inglés. Seria factibles en el idioma español

автор: Alejandro T R

2 авг. 2020 г.

Really difficult to understand the explanations of the course.

автор: Ayala A

25 июля 2020 г.

The course is good but the explanations are not clear enough.

автор: Ninder J

17 июня 2019 г.

not well explained...Rather than this go for khan's academy

автор: rajiv k K

21 июля 2019 г.

Good for rivision but I will not recommend to beginner.

автор: omri s

25 окт. 2019 г.

Good, but a lot of stuff is not explained in detail

автор: สิทธิพร แ

29 мая 2020 г.

some lessons don't cover knowledge for assignment

автор: Flávio H P d O

11 мая 2018 г.

explanation not very clear

not enought examples

автор: Rosana J B

1 мар. 2021 г.

muy confuso el sistema de envío de tareas

автор: Hiralal P

4 мая 2020 г.

they should provide more examples

автор: Neha K

9 окт. 2018 г.

The style of teaching is great.

автор: Lieu Z H

25 июля 2019 г.

found the course too basic

2 мар. 2020 г.

Good Teaching

автор: Rafael L A

9 июля 2020 г.

challenging

автор: Navya V

18 июля 2020 г.

good

автор: Amit V

8 сент. 2020 г.

1.) This is definitely not a course for beginners, especially if one does not know how to code OR if he/ she is weak in coding.

2.) As far as lectures are concerned, the faculty members/ lecturers are energetic. While some topics have been explained really well, many topics are either left without much explanation. There are some occasional mistakes on the part of faculty, which must've been edited and rectified. They have done good job in converting the lectures in to text. However, there were some mistakes in those texts too.

3.) There is no support in discussion forums from the lecturers of this course. I have seen many questions remain unanswered for many months. This is a very big drawback.

4.) There is a huge gap between what is being taught in videos and what is being asked in assignments. We can understand this by the following corollary: In the video tutorial one teacher is showing that 1 + 2 = 3. In the assignment, students are being asked to find the roots of a quadratic equation.

5.) Some questions and even their answers too technical to be understood by many students. The attempt to explain after the completion of assignment is also too technical. There should be an attempt to dive deeper to help weaker students. If time is the constraint, then make another basic course and let that be a prerequisite of this course. But please, do not mention in the introduction of this course that there is no prerequisite.

22 мая 2019 г.

It is a little messy: there are no clear definitions of Vector Space, Normed Vector Space, Euclidean Vector Space. Functions as COS and SIN are used to show basic concepts, orthogonal base, and so on. "Projection" concept always relies on base being orthogonal, projection being under 90 degree (what is 90 degree in vector space?), and space being Euclidean, although it is much simpler and applicable for just Vector Space (space without "norm" defined). Good introductory course for high-school; bad for University. Good for kids who just finished learning Pythagoras Theorem, SIN, COS, and basis of Euclidean geometry. Example of house (with number of rooms which is positive Integer number, and price which is positive Decimal) is not really a vector. Examples of non-Euclidean spaces and their applications in machine learning not provided (geometrical deep learning on graphs for example). Basic course for those completely unfamiliar with what "vector" is. Provided tests in Python are confusing because in the context we write vectors (and "base" vectors which matrix consists from) vertically, and in Python - horizontally. For example, [[1,2],[3,4]] is matrix, but it won't transform base vector [1,0] into [1,2]. This is confusing and should be mentioned before test begins.

Thank you for helping me to recall this knowledge. I finished three weeks; I may need to update review later.

автор: Mirian A

23 июля 2020 г.

Course: Definitely target for people that have solid understand of linear Algebra

Professor:

Pluses: Nice and clear voice, nice demeanor, good energy

Minuses: Long and sometimes messy samples presented on the board, not following through with the samples given (changing subjects causing confusion)

Area of improvement: It would make more interesting if would make connection with real life situation where we could make use of the classes. The instruction video made the class appealing because started with an example of a real life situation that could be resolved. It would be wonderful if full course would bring same excitement.

Exercises/Tests:

Pluses: Unfortunately there was no plus on the exercises. I hate to say that was all pretty bad.

Minus: They were confusing. A lot of time did not make connection with what was taught.

Area of improvement : Give explanation of the answers on the test itself and not referring back to the class. Resolving one to one exercise help making sense of the course being studied.

Course overall was not good. I am very glad I did not pay for this class. However I do think if the professor changes a few things he can nail this class same way he nailed the intro.

автор: eklektek

25 июля 2020 г.

The course seemed rather lazy using classical presentation methods not going the extra mile and benefitting from more model methods of visualisation and interaction. Instead the student has to hear a lot of words and try decipher the language and sketches of the speaker. I'm a native english speaker and I had problems. Complex subjects need a language that everybody can understand - visualisation.

There was finally some interactive visuals, in the fifth and final week, but these seemed more of an after thought. Also they were not integrated into the course. They would have yielded greater benefit if the lecturer used them too and pointed out specific points. Instead this information came from a few lines of explanatory text.

Generally the course material seemed like the minimum they could get away with, almost as if coursera charges hosting space.

In conclusion, the course has been beneficial, but it could have been so much more beneficial. So next I will look for a course more tightly coupled to my learning style and requirements. If this search fails I may return.