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

4.0
stars
3,144 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 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.

WS

Jul 7, 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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526 - 550 of 786 Reviews for Mathematics for Machine Learning: PCA

By Saiful B I

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May 4, 2020

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

By Sharon P

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Sep 25, 2018

Mathematically challenging, but satisfying in the end.

By Paulo Y C

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Feb 11, 2019

great material but explanation are a little bit messy

By Anas E j

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Jun 19, 2022

Thank you for this course , hope to learn more !

By taeha k

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Jul 27, 2019

Good but slightly less deeper than the other two

By Eddery L

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May 24, 2019

The instructor is great. HW setup sucks though.

By Muhammad B A

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Mar 26, 2023

its was soo hard your background not from math

By manish c

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May 6, 2020

Best course for machine learning enthusiast

By Thijs S

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Sep 28, 2020

The last assignment could use improvement.

By andre w

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Mar 27, 2022

a really good course but also really hard

By J N B P

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Sep 10, 2020

Good for intermediates in linear algebra.

By Romesh M P

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Jan 16, 2020

Too much non-video lectures (lot to read)

By Apriandi R A

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Mar 26, 2023

Overall very fun and make a little dizzy

By 3047 T

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Jul 13, 2020

The last course could have been better.

By no O

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Jul 10, 2020

Challenging but in a good way.

By Muhammad F T S

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Mar 28, 2021

this was hard but insightful

By Deleted A

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Jan 22, 2019

Good, short, overview of PCA

By Changson O

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Jan 28, 2019

Many errors of homework

By Poomphob S

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Jun 19, 2020

so challenging for me

By Sammy R

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Dec 25, 2019

Needs more details

By Ahmad Z

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Jun 11, 2025

good enough

By Shreyas S S

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Jun 20, 2020

Good Course

By NITESH J

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Aug 28, 2020

kinda long

By Egi R T

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Jul 14, 2022

Good