Back to Mathematics for Machine Learning: PCA

4.0

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1,306 ratings

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280 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 Toan T L

•Oct 03, 2018

Thank you to all the professors and staffs for such a wonderful program. I did learn a lot.

This last course is indeed a fun and challenging one. But it fells short compared to the other two due to some aspects which can be improved in the future.

Nevertheless, I'm glad that I can learn about PCA.

By Arnaud J

•Jun 12, 2018

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

By Xin W

•Nov 12, 2019

To me, the first 3 weeks in this course is good. But the 4th week is quite confusing. And I don't understand the applicable meaning for the materials in the 4th week. I may need to review what I learned in the 4th week and then decide whether I understand it completely.

By Manju S

•Jan 29, 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.

By Alexander Z

•Sep 14, 2018

Good Course, but

Too less examples to do the quizes on the first run.

Programming assignments are not clearly stated, so you need unnecessary much time to succeed.

I liked the Linear Algebra & Multivariate Modul more!

By Marina P

•Sep 06, 2019

The course is interesting, but some of the quizzes were not done very well. After the first 2 parts of this course, which were just amazing, this one seems kind of worse, although by itself its not that bad.

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 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 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 Sagun P S

•Mar 14, 2019

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

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 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