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

4.0
estrellas
2,841 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

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:

276 - 300 de 708 revisiones para Mathematics for Machine Learning: PCA

por Naggita K

19 de dic. de 2018

por Sivasankar S

3 de ago. de 2021

por Carlos E G G

28 de sep. de 2020

por Zongrui H

11 de may. de 2021

por Binu V P

8 de jun. de 2020

por Jonathon K

13 de abr. de 2020

por Xi C

31 de dic. de 2018

por Akshaya P K

25 de ene. de 2019

por Carlos A V P

15 de ene. de 2022

por Wassana K

22 de mar. de 2021

por THIRUPATHI T

24 de may. de 2020

por Eli C

21 de jul. de 2018

por Indria A

26 de mar. de 2021

por Jeff D

1 de nov. de 2020

por 任杰文

13 de may. de 2019

por Jyothula S K

18 de may. de 2020

por Thierry P

21 de abr. de 2022

por Carlos S

11 de jun. de 2018

por 祈璃

9 de jul. de 2021

por Dina B

8 de ago. de 2020

por saketh b

10 de ago. de 2020

por Sukrut B

19 de oct. de 2020

por Javas A B Y P

28 de mar. de 2021

por Israel d S R d A

5 de jun. de 2020

por Muhammad T

2 de mar. de 2021