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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: 2,338
Рецензии: 586

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.

Фильтр по:

226–250 из 582 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Akshaya P K

25 янв. 2019 г.

This was a tough course. But worth it.


24 мая 2020 г.

Thank you for offering a nice course.

автор: Eli C

21 июля 2018 г.

very challenging and rewarding course

автор: Jeff L J D

1 нояб. 2020 г.

Thank you very much for this course.

автор: 任杰文

13 мая 2019 г.

It's great, interesting and helpful.

автор: Jyothula S K

18 мая 2020 г.

Very Good Course to Learn about PCA

автор: Carlos S

11 июня 2018 г.

What you need to understand PCA!!!

автор: Dina B

8 авг. 2020 г.

Nice course - informative and fun

автор: saketh b

10 авг. 2020 г.

The instructor did a great job!

автор: Sukrut B

19 окт. 2020 г.

Try to make it little bit easy

автор: Israel d S R d A

5 июня 2020 г.

Great course very recommended

автор: Muhammad T

2 мар. 2021 г.

haha good course i completed

автор: Jonah L

6 дек. 2020 г.

It's hard but it's worth it!

автор: Gautham T

16 июня 2019 г.

excellent course by imperial

автор: Ankur A

15 мая 2020 г.

Tough course, learnt a lot.

автор: Imran S

19 дек. 2018 г.

Great Coverage of the Topic

автор: Ajay S

20 февр. 2021 г.

Great course for every one

автор: Ricardo C V

25 дек. 2019 г.

Challenging but Excellent


17 июля 2020 г.

Excellent course content

автор: Mayank K

2 июля 2020 г.

This course is very good

автор: Subhodip P

15 дек. 2020 г.

Awesome course loved it

автор: Pranav N

25 авг. 2020 г.

Amazing overall course

автор: Gazi J H

16 окт. 2020 г.

Thank you very much.

автор: Yasser Z S E

26 мая 2020 г.

Thank you very match

автор: wonseok k

3 мар. 2020 г.

hard but good course