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

4.0
stars
3,116 ratings

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.

WS

Jul 6, 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

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326 - 350 of 781 Reviews for Mathematics for Machine Learning: PCA

By Jonah L

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Dec 6, 2020

It's hard but it's worth it!

By Gautham T

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Jun 15, 2019

excellent course by imperial

By Ankur A

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May 15, 2020

Tough course, learnt a lot.

By Imran S

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Dec 19, 2018

Great Coverage of the Topic

By Ajay S

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Feb 20, 2021

Great course for every one

By Felix G S S

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Mar 27, 2021

Wow, it is so challenging

By Ricardo C V

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Dec 25, 2019

Challenging but Excellent

By CHAITANYA V

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Jul 16, 2020

Excellent course content

By Mayank K

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Jul 2, 2020

This course is very good

By Nihal T

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Jul 13, 2022

Amazing Specialization!

By Michael M

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Aug 3, 2021

I strongly recommend it

By Subhodip P

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Dec 15, 2020

Awesome course loved it

By Pranav N

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Aug 25, 2020

Amazing overall course

By iorilu

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Jun 3, 2021

intuitive and helpful

By Gazi J H

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Oct 16, 2020

Thank you very much.

By Yasser Z S E

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May 26, 2020

Thank you very match

By wonseok k

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Mar 3, 2020

hard but good course

By K F

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Sep 15, 2019

I had big fun of PCA

By Rajkumar R

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Jun 20, 2020

I enjoyed learning.

By Jason K

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Jul 23, 2021

Excellent Course !

By Omar Y B L

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Jul 15, 2020

Cruel pero justo!!

By N'guessan L R G

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Apr 14, 2020

Amazing Course!!!!

By Dominik B

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Feb 17, 2020

Great instructor!

By Sujeet B

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Jul 21, 2019

Tough, but great!

By Jitender S V

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Jul 25, 2018

AWESOME!!!!!!!!!!