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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: 2,574
Рецензии: 641

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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476–500 из 635 отзывов о курсе Mathematics for Machine Learning: PCA

автор: David N

5 мая 2021 г.

Difficult course even having completed the two courses that precede it. Some concepts introduced here as assumed knowledge that were not covered in the prior courses.

автор: Dominique D

21 июля 2020 г.

The week 2 code was more difficult than the other weeks. The forums are no longer attended by the professors. The access to materials from IC is great.

автор: Wang Z

8 июля 2018 г.

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

автор: 詹閔翔

17 янв. 2021 г.

Thank for the excellent course content but i think it would be nice if teacher could do more example or apply than just math formula introduction

автор: Iurii S

26 мар. 2018 г.

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

автор: w w

27 авг. 2020 г.

this is a great course except the assignment has quite a few bugs and the videos are too short and lack many topics, and the quiz are too short

автор: zohair a b

15 июня 2020 г.

The First 2 courses of this specialization were very good. I really wish the instructor for this course went into a little more depth.

автор: Francisco F

26 апр. 2020 г.

Average quality with low regard for intuition. Content is often Wikipedia pages or references to own content (chapters of own book).

автор: NEHAL J

21 апр. 2019 г.

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

автор: DHRUV M

3 янв. 2021 г.

Course is very high level. many concepts were not understood especially in the last course. Assignments were many confusing.

автор: Ana P A

22 апр. 2019 г.

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

автор: Chuwei L

5 апр. 2019 г.

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.

автор: SYED H

17 сент. 2020 г.

The course needs to introduce more advanced technique and practical examples or create a new Advanced course on this

автор: Jyh1003040

9 июля 2018 г.

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

автор: Hsueh-han W

20 сент. 2019 г.

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

автор: Alfian A H

25 мар. 2021 г.

I think this course has many flaws. Some of the explanation from instructor isn't very clear.

автор: Saurabh M

11 окт. 2020 г.

This course is pretty hard. The most important pre-requisite for this course is persistence.

автор: Gurudu S R

16 сент. 2019 г.

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

автор: Vishesh K

13 мар. 2020 г.

Good Content but isnt't explained well. if you are motivated by yourself then go for it.

автор: Sagun S

14 мар. 2019 г.

Tough one if you are new to programming or doesn't have excellent understanding of Maths

автор: Keng C C

30 мая 2020 г.

explanations are not clear, need to refer to lots of youtube to catch up with course.

автор: Matan A

20 окт. 2019 г.

The is a lot of gap from what the lecturer learn and what the assignments requires.

автор: Yuxuan W

5 окт. 2018 г.

Always spending much more time on coding than needed. Same result but no credit :(

автор: PS

2 мар. 2021 г.

Too much material covered too quickly. Needs to be split into seperate modules.

автор: Sethu N O G

16 авг. 2020 г.

faculty must improve his teaching techniques.

I found the course less interesting