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

4.0
estrellas
2,867 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 youâ€™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 reseÃ±as

WS

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.

JS

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:

## 626 - 650 de 712 revisiones para Mathematics for Machine Learning: PCA

por Daniel U

â€¢

27 de sep. de 2018

por amit s

â€¢

8 de feb. de 2019

por Kevin L

â€¢

11 de sep. de 2018

por shashank s

â€¢

17 de feb. de 2020

por Bohdan K

â€¢

13 de ago. de 2020

por Ivo R

â€¢

16 de nov. de 2019

por raghu c

â€¢

4 de abr. de 2020

por Paulo H S G

â€¢

27 de nov. de 2020

por Yi S

â€¢

11 de jun. de 2021

por Nidhi G

â€¢

23 de ago. de 2020

por vignesh n

â€¢

12 de sep. de 2018

por Maksim S

â€¢

25 de mar. de 2020

por Ghanem A

â€¢

20 de jul. de 2021

por Kovendhan V

â€¢

11 de jul. de 2020

por Martin H

â€¢

8 de dic. de 2019

por Jamiul H D

â€¢

7 de ago. de 2020

por Lavanith T

â€¢

21 de ago. de 2020

por Xiao L

â€¢

3 de jun. de 2019

por Sai M B

â€¢

3 de ago. de 2020

por Pawan K S

â€¢

20 de jun. de 2020

por Mohamed A H

â€¢

18 de ago. de 2021

por Kirill T

â€¢

26 de jul. de 2020

por Kevin O

â€¢

27 de mar. de 2021

por Amr F M R

â€¢

22 de sep. de 2020

por Timothy M

â€¢

22 de abr. de 2021