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学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

2,218 个评分
552 条评论


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 16, 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 18, 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.


226 - Mathematics for Machine Learning: PCA 的 250 个评论(共 549 个)

创建者 Yasser Z S E

May 26, 2020

Thank you very match

创建者 wonseok k

Mar 3, 2020

hard but good course

创建者 Keisuke F

Sep 15, 2019

I had big fun of PCA

创建者 Rajkumar R

Jun 20, 2020

I enjoyed learning.

创建者 Omar Y B L

Jul 15, 2020

Cruel pero justo!!

创建者 N'guessan L R G

Apr 14, 2020

Amazing Course!!!!

创建者 Dominik B

Feb 17, 2020

Great instructor!

创建者 Sujeet B

Jul 21, 2019

Tough, but great!

创建者 Jitender S V

Jul 25, 2018


创建者 Shanxue J

May 23, 2018

Truly exceptional

创建者 Lintao D

Sep 24, 2019

Very Good Course

创建者 Divyansh K

Nov 30, 2020

It was so tough


Nov 29, 2020

Amazing course

创建者 Shounak D

Sep 15, 2018

Great course !

创建者 Andrey

Sep 17, 2018

Great course!

创建者 Samresh

Aug 10, 2019

Nice Course.

创建者 David N

Jul 24, 2019

Great course

创建者 Snehalkumar D P

Sep 11, 2020

Nice Course

创建者 Manikant R

Jun 8, 2020

Best course

创建者 Salah T

Apr 26, 2020

Many thanks

创建者 Artur

Feb 29, 2020

good course

创建者 miguel s

Sep 20, 2020

very well

创建者 Mohamed H

Aug 10, 2019


创建者 Karthik

May 3, 2018

RRhis cl

创建者 Akash G

Mar 20, 2019