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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

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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.

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276 - 300 de 719 revisiones para Mathematics for Machine Learning: PCA

por Md. F I

8 de jun. de 2022

Good. But Programming exercise is not clear

por Hritik K S

20 de jun. de 2019

Maths is just like knowing myself very well!

por K A K

22 de may. de 2020

Learnt many new things I didn't know before

por Naggita K

19 de dic. de 2018

Great course. Rich well explained material.

por Sivasankar S

3 de ago. de 2021

This course is very informative and useful

por Carlos E G G

28 de sep. de 2020

Really difficult, but worth it in the end.

por Zongrui H

11 de may. de 2021

PCA assignment in week4 is a chanllenge!

por Binu V P

8 de jun. de 2020

best course I had ever done in coursera

por Jonathon K

13 de abr. de 2020

Great course. Extremely smart lecturer.

por Xi C

31 de dic. de 2018

Great course. Cover rigorous materials.

por Akshaya P K

25 de ene. de 2019

This was a tough course. But worth it.

por Carlos A V P

15 de ene. de 2022

Excelente curso, muy claro y retador

por Wassana K

22 de mar. de 2021

Programming Assignment is so hard !!!


24 de may. de 2020

Thank you for offering a nice course.

por Eli C

21 de jul. de 2018

very challenging and rewarding course

por Indria A

26 de mar. de 2021

very very tiring but fun, thank you.

por Jeff D

1 de nov. de 2020

Thank you very much for this course.

por 任杰文

13 de may. de 2019

It's great, interesting and helpful.

por Jyothula S K

18 de may. de 2020

Very Good Course to Learn about PCA

por Thierry P

21 de abr. de 2022

good understanding of pca insight

por Carlos S

11 de jun. de 2018

What you need to understand PCA!!!

por 祈璃

9 de jul. de 2021

This module is quite challenging!

por Dina B

8 de ago. de 2020

Nice course - informative and fun

por saketh b

10 de ago. de 2020

The instructor did a great job!

por Sukrut B

19 de oct. de 2020

Try to make it little bit easy