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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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751 - 775 of 781 Reviews for Mathematics for Machine Learning: PCA

By Ananya G

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Dec 28, 2019

I did not register in this course to have some person read out the textbooks or dictate the derivations in the lecture videos.

By Sherif B

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May 3, 2021

Very bad experience, skips steps, does not reflect on intuitions like other courses in the specializations, monotonous.

By Yap C Y

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Mar 7, 2021

Explanations need to be clearer. Efforts are needed in explaining the details of every components in this course.

By Chen H

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Jul 2, 2024

the lesson is terrible and wasting my money a lot of bugs in the assignment and I really want to call one names

By Michael K

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Nov 30, 2020

Lowest rating as the third course was absolutely poor. Low quality and in some way non-existent instruction.

By Nithin K

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Jun 5, 2018

Too conceptual and theoretical making it difficult to understand. Examples would have helped a lot.

By Nox

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Feb 19, 2024

Grading problem Even My code works perfectly and it doesn't get grade can you review plz

By Deleted A

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Jan 28, 2020

very very bad course! Assignments and quizzes made as shit. NO answers. Worth NOTHING!

By Sairam K

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Jan 9, 2021

The course videos provide insufficient and/or misleading context for the assignments.

By Daniel C

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Aug 20, 2021

the lecture videos do not seem to provide enough guidance for the assignments

By TUSHAR K

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Jul 19, 2020

Previous Two Courses were better in terms of both assignments and teaching.

By Siddharth S

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Jun 4, 2020

Very Poor when compared to previous two courses of this specialization.

By Saeif A

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Dec 31, 2019

This course was a disaster for me. The first two were great though.

By Jared E

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Aug 25, 2018

Impossible to do without apparently an indepth knowledge of python.

By Blade

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Feb 14, 2025

Full of bugs, explanation lacking, no tips. A total waste of time.

By Ricardo C F

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Nov 9, 2024

BUGS, its my second course of Imperial College with a lot of bugs

By Soumitri C

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Dec 15, 2020

okayish teaching but grading system is absolute rubbish in Week4

By Aditya P

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Jul 4, 2020

Very poor teaching and overall it's the worst course I've taken

By Ahmad O

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Aug 27, 2020

Very bad explanation. The assignments need more instructions.

By Aurel N

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Jul 5, 2020

k-NN assignment is full of errors and no proper explanations.

By Wensheng Z

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Nov 24, 2019

Jumpy instruction with little illustrations

By Adam C

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Oct 31, 2019

Worst course I've ever taken, online or IRL

By Zecheng W

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Oct 19, 2019

Poorly organized and extremely confusing

By Mingzhe D

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Dec 11, 2019

Assignment 1 cannot be passed!

By Cintya k

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Mar 2, 2021

confuse , difficuld and weird