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Вернуться к Mathematics for Machine Learning: PCA

Отзывы учащихся о курсе Mathematics for Machine Learning: PCA от партнера Имперский колледж Лондона

Оценки: 2,601
Рецензии: 646

О курсе

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Лучшие рецензии

16 июля 2018 г.

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

18 июня 2020 г.

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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376–400 из 641 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Christine W

13 авг. 2018 г.

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

автор: Shaiman S

30 апр. 2020 г.

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

автор: Karan S

1 авг. 2020 г.

Focus a bit more on PCA in week 4, week 1 was not very informative and should be assumed as required knowledge for the course

автор: Hilmi E

20 апр. 2020 г.

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

автор: Voravich C

21 окт. 2019 г.

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

автор: Phuong N

17 окт. 2018 г.

That's a great online courses can help people have enough background to break into Machine Learning or Data science

автор: Ananthesh J S

16 июня 2018 г.

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

автор: Manuel I

7 июля 2018 г.

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

автор: Shraavan S

4 мар. 2019 г.

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

автор: Andrew D

2 июня 2019 г.

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

автор: Neelam J U

23 сент. 2020 г.

The programming assignments were quite challenging. Some part of the course can discuss this aspect as well.

автор: Paulo N A J

18 авг. 2020 г.

It is a good course with hard programming, but the assignments could be improved. The forum helps a lot.

автор: Ibon U E

7 янв. 2020 г.

The derivations of some concepts have been more vague compared to other courses in this specialization.

автор: Max W

19 апр. 2020 г.

Very challenging, could have used a few more videos to really explain or give a few more examples

автор: Abhishek T

12 апр. 2020 г.

The structure could have been better. Some of the weeks were too crowded as compared to others

автор: Phuong A V

7 авг. 2020 г.

very difficult course. But I hope that it will be useful fore my machine learning studying

автор: kerryliu

30 июля 2018 г.

still have room for improvement since lots of stuffs can be discussed more in detail.

автор: Ruan v S

13 окт. 2019 г.

Harder than expected, the content is good and is well worth the struggle!

автор: Xin W

6 сент. 2019 г.

This course is full of mathematical derivation, so it is kind of boring.

автор: Bintang K P P

26 мар. 2021 г.

We need more basic example and exercise before taking graded assignment

автор: Felipe T B

10 авг. 2020 г.

Computational exercises could have more support from the professors.

автор: Jiaxuan L

15 июля 2019 г.

Overall a good course. Very limited introduction to Python though.

автор: Chow K M

28 июля 2020 г.

Quite challenging. Need to keep notes for programming assignment.

автор: Lafite

4 февр. 2019 г.


автор: Attili S

19 авг. 2020 г.

Great course! It could have elaborated more in the week 4 PCA