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Learner Reviews & Feedback for Mathematics for Machine Learning: PCA by Imperial College London

4.0
stars
2,214 ratings
551 reviews

About the Course

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....

Top reviews

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.

NS
Jun 18, 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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301 - 325 of 547 Reviews for Mathematics for Machine Learning: PCA

By Lotachukwu I

Jun 27, 2020

Very challenging at times, but very good course none the less. Would recommend to any one who has a solid foundation of Linear Algebra (Course 1) and Multivariate Calculus (Course 2).

By Zhou W

Oct 4, 2020

Fascinating course! The lecturer gives very detailed illustrations to many complicate concepts. It will be much better if the submitting systems work fine for the last assignment.

By Faisal

May 28, 2020

Course content is interesting and well planned, Can be improved by making it Simpler for Students as it was more technical than the other 2 courses of the Specialization.

By Tarik R

Sep 10, 2020

it's very fantastic course.i enjoyed a lot.i feel reading material should be increases in those courses,others things are perfectly ok.thanks for offering this courses.

By Abhishek P

Sep 9, 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 Hadhrami A G

Jun 7, 2020

The course is generally good but the assignment setting definitely needs to be rectified. Thanks anyway for this course. An important element of machine learning.

By Liang S

Jul 9, 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 Kevin E

Aug 27, 2020

Overall the course was great. The only thing was that there was a lot I didn't understand from the videos. The recommended textbook resource was a great help.

By Ezequiel P

Sep 26, 2020

The other two courses were much more didactic. And there were some bugs in these courses assignments... But, overall, it was a great course on the subject

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 5, 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 Helena S

Feb 28, 2020

The final Notebook contains some errors (Xbar instead of X, passed as an argument). Otherwise a very well organized course. Thanks a lot!

By Giri G

Jun 7, 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 Leon T

Jul 10, 2020

Jupyter notebook assignments are in desperate need of attention! Very buggy or non-intuitive for the scope of material in span of time.

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 Shaiman S

Apr 30, 2020

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

By Karan S

Aug 1, 2020

Focus a bit more on PCA in week 4, week 1 was not very informative and should be assumed as required knowledge for the course

By Hilmi E

Apr 20, 2020

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

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 7, 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 4, 2019

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