Chevron Left
Volver a Mathematics for Machine Learning: PCA

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

2,865 calificaciones

Acerca del Curso

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....

Principales reseñas


6 de jul. de 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.


16 de jul. de 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.

Filtrar por:

376 - 400 de 711 revisiones para Mathematics for Machine Learning: PCA

por Zax

27 de jul. de 2021

por Aileen F

14 de dic. de 2020

por Nikolay B

3 de ago. de 2019

por Raul B M

1 de nov. de 2020

por Luke L

11 de jun. de 2020

por Claudio P

24 de ene. de 2020

por Rob O

3 de ago. de 2020

por Barnaby D

3 de ene. de 2020

por Jorge G

15 de sep. de 2020

por Nelson F A

25 de abr. de 2019

por Visveswara K M

18 de jun. de 2020

por greg m

24 de may. de 2020

por Evgeny ( C

25 de jul. de 2018

por Mark S

7 de jul. de 2018

por Jérôme M

26 de jul. de 2018

por Timo K

10 de abr. de 2018

por Joshua B A

11 de mar. de 2019

por Florian C

20 de jun. de 2021

por Felipe C

9 de sep. de 2021

por Cheng T Y

8 de jul. de 2018

por Phạm N M H

12 de jul. de 2019

por Thorben S

8 de mar. de 2019

por Jia J W

2 de dic. de 2020

por Andrés M

4 de jul. de 2020

por Mike W

22 de mar. de 2020