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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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751 - 775 of 776 Reviews for Mathematics for Machine Learning: PCA

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 10, 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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Jan 1, 2020

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 Ricardo C F

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Nov 10, 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 20, 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 3, 2021

confuse , difficuld and weird

By 朱嘉懿

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Jun 25, 2020

The assignment worked badly.

By Syed s A

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Jul 23, 2020

Assignment is not proper

By Анофриев А

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Oct 1, 2019

The worst course ever

By Bohdan S

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Feb 17, 2020

Worst course ever

By Ankit M

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Jul 12, 2020

POOR VERY POOR