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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: 2,274
Рецензии: 569

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:

176–200 из 564 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Alfonso J

20 окт. 2019 г.

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

автор: MD K A

8 авг. 2020 г.

Algebra, Calculus and PCA

These are all excellent, if you have mathematics knowledge

автор: Arijit B

5 нояб. 2019 г.

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

автор: ELINGUI P U

25 мая 2018 г.

Very hard to follow, but you need to do it to understand machine learning very well.

автор: Greg E

27 июля 2019 г.

I have thoroughly enjoyed every course of this specialization. Thank you very much.

автор: Faruk Y

22 сент. 2019 г.

Lectures and programming assignments were selected nicely to teach the math of PCA

автор: Sanjay B

30 дек. 2020 г.

Excellent program, helped get to understand features of Python programming fast

автор: Lia L

22 мая 2019 г.

This was really difficoult, but I'm so proud for the completion of the course.

автор: Pritam C

22 сент. 2020 г.

It was an intense Math Class with a piece of new knowledge about PCA...Thanks

автор: Roshan C

23 нояб. 2019 г.

the course was very much intuitive and helpful to grasp the knowledge of PCA

автор: Pramod H K

7 авг. 2020 г.

The highly mathematical perspective of PCA with greater conceptualization.

автор: Rishabh A

17 июня 2019 г.

We need more elaborate explanation at few tricky places during the course.

автор: Aman M

1 июля 2020 г.

good content but assignment quality and maintenance should be rechecked

автор: Seelam S

25 июля 2020 г.

Good Course to get knowledge of Maths required for Machine Learning! ☺

автор: Sanchayan D

7 июня 2020 г.

Good Introduction to understanding the principal component analysis

автор: Benjamin C

28 янв. 2020 г.

Excellent course regarding both theoritical and practical sides.

автор: Shahriyar R

14 сент. 2019 г.

The hardest one but still useful, very informative neat concepts

автор: J G

12 мая 2018 г.

This is a good course, you learn about the foundations of PCA.

автор: Opas S

15 июля 2020 г.

Great course for improve math skilled and improve basic to ML

автор: Isaac M M

9 авг. 2020 г.

A bit more difficult than previous ones but it is worth it

автор: Phani B R P

1 июня 2020 г.

Very good course and extremely challenging, especially PCA

автор: Anh V

15 нояб. 2020 г.

Very detailed explanation and mathematics underlying PCA!

автор: Md A A M

24 авг. 2020 г.

Great Course. Everyone should take this course. Thanks.

автор: Harish S

24 нояб. 2019 г.

This was a difficult course but still very informative.

автор: Oleg B

6 янв. 2019 г.

Excellent focus on important topics that lead up to PCA