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

4.0
stars
2,170 ratings
536 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.

NS

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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301 - 325 of 531 Reviews for Mathematics for Machine Learning: PCA

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 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 01, 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 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 Neelam J U

Sep 23, 2020

The programming assignments were quite challenging. Some part of the course can discuss this aspect as well.

By Paulo N A J

Aug 18, 2020

It is a good course with hard programming, but the assignments could be improved. The forum helps a lot.

By Ibon U E

Jan 07, 2020

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

By Max W

Apr 20, 2020

Very challenging, could have used a few more videos to really explain or give a few more examples

By Abhishek T

Apr 12, 2020

The structure could have been better. Some of the weeks were too crowded as compared to others

By Phuong A N

Aug 07, 2020

very difficult course. But I hope that it will be useful fore my machine learning studying

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 Felipe T B

Aug 11, 2020

Computational exercises could have more support from the professors.

By Jiaxuan L

Jul 15, 2019

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

By Chow K M

Jul 29, 2020

Quite challenging. Need to keep notes for programming assignment.

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