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Back to Mathematics for Machine Learning: PCA

Learner Reviews & Feedback for Mathematics for Machine Learning: PCA by Imperial College London

4.0
stars
2,322 ratings
•
582 reviews

About the Course

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....

Top reviews

JS
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.

NS
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.

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251 - 275 of 577 Reviews for Mathematics for Machine Learning: PCA

By Omar Y B L

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Jul 15, 2020

Cruel pero justo!!

By N'guessan L R G

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Apr 14, 2020

Amazing Course!!!!

By Dominik B

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Feb 17, 2020

Great instructor!

By Sujeet B

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Jul 21, 2019

Tough, but great!

By Jitender S V

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Jul 25, 2018

AWESOME!!!!!!!!!!

By Shanxue J

•
May 23, 2018

Truly exceptional

By Lintao D

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Sep 24, 2019

Very Good Course

By Divyansh K

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Nov 30, 2020

It was so tough

By EDWARD J R

•
Nov 29, 2020

Amazing course

By Shounak D

•
Sep 15, 2018

Great course !

By Andrey

•
Sep 17, 2018

Great course!

By Samresh

•
Aug 10, 2019

Nice Course.

By David N

•
Jul 24, 2019

Great course

By Snehal P

•
Sep 11, 2020

Nice Course

By Manikant R

•
Jun 8, 2020

Best course

By Salah T

•
Apr 26, 2020

Many thanks

By Artur

•
Feb 29, 2020

good course

By miguel s

•
Sep 20, 2020

very well

By Mohamed H

•
Aug 10, 2019

fantastic

By Karthik

•
May 3, 2018

RRhis cl

By Akash G

•
Mar 20, 2019

awesome

By Bálint - H F

•
Mar 20, 2019

Great !

By Md. R Q S

•
Aug 21, 2020

great

By GEETHA P

•
Jul 28, 2020

good

By RAGHUVEER S D

•
Jul 25, 2020

good

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