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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

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1,915 个评分
457 条评论

课程概述

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....

热门审阅

JS

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.

NS

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.

筛选依据:

176 - Mathematics for Machine Learning: PCA 的 200 个评论(共 455 个)

创建者 Akshaya P K

Jan 25, 2019

This was a tough course. But worth it.

创建者 THIRUPATHI T

May 24, 2020

Thank you for offering a nice course.

创建者 Eli C

Jul 22, 2018

very challenging and rewarding course

创建者 任杰文

May 13, 2019

It's great, interesting and helpful.

创建者 Jyothula S K

May 18, 2020

Very Good Course to Learn about PCA

创建者 Carlos S

Jun 11, 2018

What you need to understand PCA!!!

创建者 Israel d S R d A

Jun 05, 2020

Great course very recommended

创建者 Gautham T

Jun 16, 2019

excellent course by imperial

创建者 Ankur A

May 15, 2020

Tough course, learnt a lot.

创建者 imran s

Dec 20, 2018

Great Coverage of the Topic

创建者 Ajay S

Apr 09, 2019

Great course for every one

创建者 Ricardo C V

Dec 25, 2019

Challenging but Excellent

创建者 CHAITANYA V

Jul 17, 2020

Excellent course content

创建者 MAYANK K

Jul 02, 2020

This course is very good

创建者 Yasser Z S E

May 26, 2020

Thank you very match

创建者 wonseok k

Mar 03, 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 15, 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

AWESOME!!!!!!!!!!

创建者 Shanxue J

May 23, 2018

Truly exceptional

创建者 Lintao D

Sep 24, 2019

Very Good Course