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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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276 - 300 of 776 Reviews for Mathematics for Machine Learning: PCA

By Opas S

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Jul 16, 2020

Great course for improve math skilled and improve basic to ML

By Puja P N B M

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Mar 29, 2022

PCA assigment i dont have ideas but overall course is good

By Isaac M M

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Aug 9, 2020

A bit more difficult than previous ones but it is worth it

By Phani B R P

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Jun 1, 2020

Very good course and extremely challenging, especially PCA

By Anh V

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Nov 15, 2020

Very detailed explanation and mathematics underlying PCA!

By Md. A A M

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Aug 24, 2020

Great Course. Everyone should take this course. Thanks.

By Harish S

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Nov 24, 2019

This was a difficult course but still very informative.

By Oleg B

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Jan 6, 2019

Excellent focus on important topics that lead up to PCA

By Kaustubh S

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Nov 29, 2020

Very tough course but got a good sense of what PCA is

By Prateek S

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Jul 14, 2020

best course and important to study with concentration

By Lahiru D

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Sep 16, 2019

Great course. Assignments are tough and challenging.

By Archana D

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Mar 6, 2020

Brilliant work, references and formulas aided a lot

By Tich M

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Jan 18, 2019

good course, rigorous proof and practical exercises

By Goh K L

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Aug 8, 2021

Decently challenging and therefore very fruitful.

By Diego S

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May 2, 2018

Difficult! But I did it :D And I learnt a lot...

By Ida B R A M M

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Mar 27, 2022

Very HARD but fundamentals are important, yes?

By Levi C

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Feb 3, 2020

A good representation after preceding courses.

By Wang S

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Oct 21, 2019

A little bit difficult but helpful, thank you!

By eder p g

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Aug 9, 2020

excellent!!!! it's very useful and practical.

By Murugesan M

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Jan 15, 2020

Excellent! very intuitive learning approach!!

By Mohd. F I S

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Jun 8, 2022

Good. But Programming exercise is not clear

By Hritik K S

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Jun 20, 2019

Maths is just like knowing myself very well!

By K A K

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May 22, 2020

Learnt many new things I didn't know before

By Naggita K

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Dec 19, 2018

Great course. Rich well explained material.

By Sivasankar S

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Aug 3, 2021

This course is very informative and useful