Back to Mathematics for Machine Learning: PCA

4.0

stars

1,312 ratings

•

284 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 Suyog P

•Sep 02, 2019

Finally understood basic intuition of PCA, never got perfect resource before. However, there was a sharp change in terms of course delivery than the previous two courses of this specialization. So, heads up.

By Camilo J

•Mar 01, 2019

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

By Abhishek P

•Sep 09, 2019

Course content tackles a difficult topic well. Only flaw is that programming assignments are poorly designed in some places and are quite difficult to pick up at times.

By Liang S

•Jul 09, 2018

Teaching pacing is good, and clear in explanation. It will be good if there are some examples about how we should apply all these theories to some real problems.

By Mohamed B

•Oct 27, 2019

I learned a lot in this course, though the last week was somehow hurried and the lecturer didn't spend enough time to piece the whole stuff together.

By Rok Z

•Feb 05, 2020

A different course than the previous 2.

Much harder - as you have to actually know some Python tricks.

But I guess it's the same in a real world.

By Jordan V

•Aug 23, 2019

Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.

By Kevin G

•Jan 14, 2020

Felt like explanations in this course were a bit confusing, but otherwise, it was a very interesting course. Thank you so much for doing this.

By Giri G

•Jun 07, 2019

This was a very hard course for me. But I think the instructor has done the best possible he can with presenting and explaining the course

By Christine W

•Aug 13, 2018

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

By Voravich C

•Oct 21, 2019

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

By Nguyen D P

•Oct 17, 2018

That's a great online courses can help people have enough background to break into Machine Learning or Data science

By Ananthesh J S

•Jun 16, 2018

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

By Manuel I

•Jul 07, 2018

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

By Shraavan S

•Mar 04, 2019

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

By Andrew D

•Jun 02, 2019

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

By Ibon U E

•Jan 07, 2020

The derivations of some concepts have been more vague compared to other courses in this specialization.

By Berkay E

•Aug 09, 2019

-Some of the contents are not clear.

+It gets great intuition for new learners in machine learning.

By sairavikanth t

•Apr 29, 2018

Lot of Math. Couldn't get proper intuition regarding PCA, was lost in understanding math equations

By Jessica P

•Aug 06, 2019

I agree with the others. Course didn't merge well with the 1st two which were perfect!

By kerryliu

•Jul 30, 2018

still have room for improvement since lots of stuffs can be discussed more in detail.

By Ruan v S

•Oct 14, 2019

Harder than expected, the content is good and is well worth the struggle!

By Xin W

•Sep 06, 2019

This course is full of mathematical derivation, so it is kind of boring.

By Jiaxuan L

•Jul 15, 2019

Overall a good course. Very limited introduction to Python though.

By Lafite

•Feb 04, 2019

编程练习的质量不够高，不管是编程练习本身的代码逻辑、注释、练习的质量还是在答疑区课程组的答疑都不能尽如人意，对于编程练习并不很满意

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