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

4.0
1,176 ratings
248 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 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.

JV

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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151 - 175 of 246 Reviews for Mathematics for Machine Learning: PCA

By Eddery L

May 24, 2019

The instructor is great. HW setup sucks though.

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 Berkay E

Aug 09, 2019

-Some of the contents are not clear.

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

By João M G

Aug 14, 2019

The course was great till the final week. The lectures did not explain very well the concepts and the assignment was poorly designed. It's a shame because I've loved the more rigorous way of this final course.

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 Phạm N M H

Jul 12, 2019

This maybe the most frustrating course and most advance compare to 2 other courses, you might confuse about the code in the assignment of this course. So, if you do have basic background about coding with numpy, matrices,etc..., I do recommend this course, if you qualify enough to fix the bugs of what the dev team left.

By Jiaxuan L

Jul 15, 2019

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

By ranzhang

Aug 29, 2019

I think it's really a hard lesson for me, but I've also learn a lot, thanks a lot for the teacher and coursera. Some Programming test take too long to execute, and there are some errors in it. just be careful

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 Xin W

Sep 06, 2019

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

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 Shariq A

Oct 20, 2019

Thank you professor for providing such a valuable course.

Just I wanted to say one thing without hurting anyone, the week 4 on PCA is not very clear. The derivation are not very correlated .A humble request isthat to elaborate the derivation which would further enhance the learning

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 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 Gaetano F

Oct 10, 2019

I found the course excellent but in the programming assignments is not always clear what should one exactly do. They are also quite confusing, especially the last one on PCA implementation. One wastes so much time trying to figure out the solution.

By Ruan v S

Oct 14, 2019

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

By Kevin G

Nov 29, 2019

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 k v k

Nov 30, 2018

its a good course to learn mathematics essential for machine learning

By Prashant D

Feb 17, 2019

The lecturer is good and probably has a very good understanding of the mathematics. However if you are looking for a light and easy course, then this one is not for you. The mathematics is sometimes difficult to follow and although the lecturer patiently explains the derivation of the results, I had to go back and forth a number of times to understand what was happening.

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 Chuwei L

Apr 05, 2019

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

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 Sagun P S

Mar 14, 2019

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

By Malcolm M

Mar 06, 2019

Far more challenging than the first two courses.

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!