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

4.0
stars
3,108 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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376 - 400 of 776 Reviews for Mathematics for Machine Learning: PCA

By Sudarshan J

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Feb 12, 2023

great !

By Levina A

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Mar 28, 2021

So cool

By alfatoni n

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Mar 12, 2021

Finally

By Akash G

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Mar 20, 2019

awesome

By Bálint - H F

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Mar 20, 2019

Great !

By RAHMITA D K

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Mar 30, 2023

Mantep

By Sean F

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Jun 22, 2021

Tough.

By Ahmad H A

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Mar 27, 2022

great

By Insyiraah O A

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Mar 26, 2022

GREAT

By Mellania P S

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Mar 23, 2021

great

By Indah D S

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Mar 9, 2021

great

By Md. R Q S

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Aug 21, 2020

great

By Data I M

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May 11, 2023

nope

By Marsella D U

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Mar 28, 2023

nice

By Fitrah S

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Mar 20, 2023

cool

By Faisal A M

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Apr 11, 2022

good

By Doni S

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Mar 27, 2022

Good

By Suci A S

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Jun 20, 2021

GOOD

By Agung W

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Mar 28, 2021

nice

By Ahmad H N

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Mar 21, 2021

Good

By GEETHA P

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Jul 28, 2020

good

By RAGHUVEER S D

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Jul 25, 2020

good

By Harsh D

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Jun 28, 2018

good

By Amini D P S

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Mar 27, 2022

wow

By mochammad g r

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Mar 25, 2021

@.@