Back to Mathematics for Machine Learning: PCA

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2,669 ratings

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671 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....

WS

Jul 6, 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

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.

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By Goh K L

•Aug 8, 2021

Decently challenging and therefore very fruitful.

By Diego S

•May 2, 2018

Difficult! But I did it :D And I learnt a lot...

By CHIOU Y C

•Feb 3, 2020

A good representation after preceding courses.

By Wang S

•Oct 21, 2019

A little bit difficult but helpful, thank you!

By eder p g

•Aug 9, 2020

excellent!!!! it's very useful and practical.

By Murugesan M

•Jan 15, 2020

Excellent! very intuitive learning approach!!

By Hritik K S

•Jun 20, 2019

Maths is just like knowing myself very well!

By K A K

•May 22, 2020

Learnt many new things I didn't know before

By Naggita K

•Dec 19, 2018

Great course. Rich well explained material.

By Sivasankar S

•Aug 3, 2021

This course is very informative and useful

By Carlos E G G

•Sep 28, 2020

Really difficult, but worth it in the end.

By Zongrui H

•May 11, 2021

PCA assignment in week4 is a chanllenge!

By Binu V P

•Jun 8, 2020

best course I had ever done in coursera

By Jonathon K

•Apr 13, 2020

Great course. Extremely smart lecturer.

By Xi C

•Dec 31, 2018

Great course. Cover rigorous materials.

By Akshay K

•Jan 25, 2019

This was a tough course. But worth it.

By Wassana K

•Mar 22, 2021

Programming Assignment is so hard !!!

By THIRUPATHI T

•May 24, 2020

Thank you for offering a nice course.

By Eli C

•Jul 21, 2018

very challenging and rewarding course

By Indria A

•Mar 26, 2021

very very tiring but fun, thank you.

By Jeff D

•Nov 1, 2020

Thank you very much for this course.

By 任杰文

•May 13, 2019

It's great, interesting and helpful.

By Jyothula S K

•May 18, 2020

Very Good Course to Learn about PCA

By Carlos S

•Jun 11, 2018

What you need to understand PCA!!!

By Qili

•Jul 9, 2021

This module is quite challenging!

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