Chevron Left
Back to Mathematics for Machine Learning: PCA

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

4.0
stars
2,867 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.

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.

Filter by:

601 - 625 of 712 Reviews for Mathematics for Machine Learning: PCA

By Felipe R D

Aug 20, 2021

By Felipe M

Jul 26, 2020

By Pedro L

Apr 25, 2020

By Jim F

Apr 12, 2021

By Abhishek J

Jul 30, 2020

By Tuan Q N

Feb 16, 2021

By Erik P

Feb 12, 2020

By Jonathan M

Jan 23, 2021

By Nicholas T

Aug 31, 2020

By noel s

Jul 22, 2020

By Brian G

Mar 12, 2021

By Sagar L

Mar 21, 2020

By Vitali Z

Aug 22, 2020

By Toby T

Jul 14, 2019

By Mark C

Jul 30, 2018

By Max B

Aug 14, 2019

By Nouran G

Oct 11, 2018

By Marvin P

Apr 24, 2018

By Michael D

Jul 22, 2019

By Ricardo F

Mar 4, 2021

By Dan A

May 9, 2020

By NamTPSE150004

Feb 11, 2021

By Muhammad F I

Mar 25, 2022

By Xiaoxiao L

Jan 4, 2021

By Alois H

Feb 18, 2021