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

4.0
stars
3,158 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

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.

CH

Dec 27, 2019

This course is well worth the time. I have a better understanding of one of the most foundational and biologically plausible machine learning algorithms used today! Love it.

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776 - 790 of 790 Reviews for Mathematics for Machine Learning: PCA

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

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

By Arjunsiva S

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Oct 4, 2020

meh!