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

4.0
stars
2,282 ratings
572 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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351 - 375 of 569 Reviews for Mathematics for Machine Learning: PCA

By Xin W

Sep 6, 2019

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

By Felipe T B

Aug 10, 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 28, 2020

Quite challenging. Need to keep notes for programming assignment.

By Lafite

Feb 4, 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

By Attili S

Aug 19, 2020

Great course! It could have elaborated more in the week 4 PCA

By Ashok B B

Feb 6, 2020

Course was challenging , but learned the maths behind PCA,

By Cesar A P C J

Dec 23, 2018

Good content, just need to fix the assignments' platform.

By Dave D

May 30, 2020

This course was a fair overview of a very complex topic.

By ADITYA K

May 13, 2020

It is very informative and hands-on based Course for PCA

By Md. S B S

May 4, 2020

Not as good as the other two courses..but interesting!

By Sharon P

Sep 24, 2018

Mathematically challenging, but satisfying in the end.

By Paulo Y C

Feb 11, 2019

great material but explanation are a little bit messy

By taeha k

Jul 27, 2019

Good but slightly less deeper than the other two

By Eddery L

May 24, 2019

The instructor is great. HW setup sucks though.

By Manish C

May 6, 2020

Best course for machine learning enthusiast

By Thijs S

Sep 28, 2020

The last assignment could use improvement.

By J N B P

Sep 10, 2020

Good for intermediates in linear algebra.

By Romesh M P

Jan 16, 2020

Too much non-video lectures (lot to read)

By Tanmoy S

Jul 13, 2020

The last course could have been better.

By Kailash Y

Jul 9, 2020

Challenging but in a good way.

By Mark R

Jan 22, 2019

Good, short, overview of PCA

By Changxin W

Jan 28, 2019

Many errors of homework

By Poomphob S

Jun 18, 2020

so challenging for me

By Sammy R

Dec 25, 2019

Needs more details