Back to Mathematics for Machine Learning: PCA

4.0

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1,933 ratings

•

465 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.

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.

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By Cécile L

•Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

By Nicholas K

•Apr 28, 2018

It's a shame. There's lots of good material and I learned a lot. But a staggering amount of time was wasted figuring out gaps in the instructions - portions felt more like hazing than teaching.

By Adrian C

•Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

By Jean D D S

•Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

By Dominique D

•Jul 22, 2020

The week 2 code was more difficult than the other weeks. The forums are no longer attended by the professors. The access to materials from IC is great.

By Wang Z

•Jul 08, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

By Iurii S

•Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

By zohair a b

•Jun 16, 2020

The First 2 courses of this specialization were very good. I really wish the instructor for this course went into a little more depth.

By Francisco F

•Apr 26, 2020

Average quality with low regard for intuition. Content is often Wikipedia pages or references to own content (chapters of own book).

By NEHAL J

•Apr 21, 2019

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

By Ana P A

•Apr 22, 2019

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

By Chuwei L

•Apr 05, 2019

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.

By Jyh1003040

•Jul 09, 2018

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

By Hsueh-han W

•Sep 20, 2019

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

By Gurudu S R

•Sep 16, 2019

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

By Vishesh K

•Mar 13, 2020

Good Content but isnt't explained well. if you are motivated by yourself then go for it.

By Sagun P S

•Mar 14, 2019

Tough one if you are new to programming or doesn't have excellent understanding of Maths

By Keng C C

•May 30, 2020

explanations are not clear, need to refer to lots of youtube to catch up with course.

By Matan A

•Oct 20, 2019

The is a lot of gap from what the lecturer learn and what the assignments requires.

By Yuxuan W

•Oct 05, 2018

Always spending much more time on coding than needed. Same result but no credit :(

By Rafael C

•Dec 07, 2019

definitely one of the most catastrophic courses I've ever taken on Coursera...

By Meraldo A

•May 08, 2018

The course content was good; however, it was not well explained at times.

By connie

•Mar 21, 2020

I think content of first 2 weeks are disconnect with 3rd and 4th weeks

By Alexander

•Nov 06, 2019

Math for the sake of math. Too big jumps in calculations, too complex.

By k v k

•Nov 30, 2018

its a good course to learn mathematics essential for machine learning

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