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學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

2,164 個評分
536 條評論


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....



Jul 17, 2018

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.


Jun 19, 2020

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.


201 - Mathematics for Machine Learning: PCA 的 225 個評論(共 531 個)

創建者 Jyothula S K

May 18, 2020

Very Good Course to Learn about PCA

創建者 Carlos S

Jun 11, 2018

What you need to understand PCA!!!

創建者 Dina B

Aug 08, 2020

Nice course - informative and fun

創建者 saketh b

Aug 10, 2020

The instructor did a great job!

創建者 Sukrut S B

Oct 19, 2020

Try to make it little bit easy

創建者 Israel d S R d A

Jun 05, 2020

Great course very recommended

創建者 Gautham T

Jun 16, 2019

excellent course by imperial

創建者 Ankur A

May 15, 2020

Tough course, learnt a lot.

創建者 imran s

Dec 20, 2018

Great Coverage of the Topic

創建者 Ajay S

Apr 09, 2019

Great course for every one

創建者 Ricardo C V

Dec 25, 2019

Challenging but Excellent


Jul 17, 2020

Excellent course content


Jul 02, 2020

This course is very good

創建者 Pranav N

Aug 25, 2020

Amazing overall course

創建者 Gazi J H

Oct 16, 2020

Thank you very much.

創建者 Yasser Z S E

May 26, 2020

Thank you very match

創建者 wonseok k

Mar 03, 2020

hard but good course

創建者 Keisuke F

Sep 15, 2019

I had big fun of PCA

創建者 Rajkumar R

Jun 20, 2020

I enjoyed learning.

創建者 Omar Y B L

Jul 15, 2020

Cruel pero justo!!

創建者 N'guessan L R G

Apr 15, 2020

Amazing Course!!!!

創建者 Dominik B

Feb 17, 2020

Great instructor!

創建者 Sujeet B

Jul 21, 2019

Tough, but great!

創建者 Jitender S V

Jul 25, 2018


創建者 Shanxue J

May 23, 2018

Truly exceptional