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
2,841 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:

326 - 350 of 707 Reviews for Mathematics for Machine Learning: PCA

By Shanxue J

May 23, 2018

By Sabrina M U

Mar 27, 2021

By Habib B K

Mar 13, 2021

By Lintao D

Sep 24, 2019

By Divyansh K

Nov 30, 2020

By Firli A R

Mar 27, 2022

By EDWARD J R

Nov 29, 2020

By Shounak D

Sep 15, 2018

By Andrey

Sep 17, 2018

By Samresh

Aug 10, 2019

By David N

Jul 24, 2019

By Snehal P

Sep 11, 2020

By Manikant R

Jun 8, 2020

By Salah T

Apr 26, 2020

By Artur

Feb 29, 2020

By Bintang F E

Mar 28, 2021

By Muhammad T R T P

Mar 28, 2021

By Andreanov R

Mar 15, 2021

By miguel s

Sep 20, 2020

By Mohamed H

Aug 10, 2019

By Karthik

May 3, 2018

By Levina A

Mar 28, 2021

By Al F N P

Mar 12, 2021

By Akash G

Mar 20, 2019

By Bálint - H F

Mar 20, 2019