Back to Mathematics for Machine Learning: PCA

4.0

stars

1,263 ratings

•

268 reviews

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.

May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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By Naggita K

•Dec 19, 2018

Great course. Rich well explained material.

By Xi C

•Dec 31, 2018

Great course. Cover rigorous materials.

By Akshaya P K

•Jan 25, 2019

This was a tough course. But worth it.

By Eli C

•Jul 22, 2018

very challenging and rewarding course

By 任杰文

•May 13, 2019

It's great, interesting and helpful.

By Carlos S

•Jun 11, 2018

What you need to understand PCA!!!

By Gautham T

•Jun 16, 2019

excellent course by imperial

By imran s

•Dec 20, 2018

Great Coverage of the Topic

By Ajay S

•Apr 09, 2019

Great course for every one

By Ricardo C V

•Dec 25, 2019

Challenging but Excellent

By Keisuke F

•Sep 15, 2019

I had big fun of PCA

By Sujeet B

•Jul 21, 2019

Tough, but great!

By Jitender S V

•Jul 25, 2018

AWESOME!!!!!!!!!!

By Shanxue J

•May 23, 2018

Truly exceptional

By Lintao D

•Sep 24, 2019

Very Good 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 Mohamed H

•Aug 10, 2019

fantastic

By Karthik

•May 03, 2018

RRhis cl

By Akash G

•Mar 20, 2019

awesome

By Bálint - H F

•Mar 20, 2019

Great !

By HARSH K D

•Jun 28, 2018

good

By Vassiliy T

•Jul 10, 2018

it is good, challenging course. i've learned a lot, but feel that i came away with quite patchy knowledge. This course is a big step up in complexity and delivery form the previous two courses. perhaps my expectations were not right to start with - one cannot learn this level of complexity so quickly. Admittedly there are many gaps between the lectures and course materials and what is asked in programming assignments. i ended up reading a lot online to fill in the gaps (i've learned a lot of python during the course, which is great!).nevertheless, after this course i feel equipped to continue with machine learning.