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Voltar para Mathematics for Machine Learning: PCA

Comentários e feedback de alunos de Mathematics for Machine Learning: PCA da instituição Imperial College London

4.0
estrelas
2,168 classificações
536 avaliações

Sobre o 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....

Melhores avaliações

JS

Jul 17, 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.

NS

Jun 19, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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501 — 525 de 531 Avaliações para o Mathematics for Machine Learning: PCA

por Ed W

Nov 25, 2019

The lectures gave incomplete information for the understanding of the material and the homework assignments. Wish this course was stretched to be a 10 week course so that we can all thoroughly learn the material.

por Christiano d S

Aug 10, 2020

The lessons are not clear and if one wants to learn and understand what is going on with the math/algebra, has to study with other resources, because the videos of this course just throw up info´s on screen.

por Kimberely C

Dec 27, 2019

Definitely, not for beginners. Just as bad as the last one. They need to have more examples, which walk you through the ones like they give you on the homework as well as an example of how to do Python.

por Gurrapu N

Apr 09, 2020

There is hardly any co-relation between videos and assignments, while the lectures were at high school level but the assignments were at graduate level. It is high time to revise the course contents.

por Marcin

Aug 19, 2018

By far the worst online course that I've ever done. Assignments require a lot of experience in Python, which is not communicated upfront. At the same time, staff doesn't provide any actual support.

por Danielius K

Sep 24, 2019

You will spend most of your time lost.

Quizes are not clear and ill-prepared.

You will need to spend a lot of time looking for material outside of the course to actually make progress.

por Saransh G

Apr 28, 2020

1. Not intuitive like first two programs

2. The assignments sometimes jumped concepts and were not cohesive

3. The in-lecture problems seemed rushed through

por Tai J Y

Nov 16, 2019

This course is not like other two, which explain much clearly. When I do the practice quiz and coding, I resort to find other help on the Internet.

por Vibhutesh K S

May 18, 2019

This course is really bad and extremely hard to follow. Previous two courses were executed very well, teaching quality in this is poor.

por Alejandro T R

Aug 02, 2020

Worst of the three courses. I learned much more on the internet because of the lack of examples or explanation. Just not worth it.

por Ananya G

Dec 28, 2019

I did not register in this course to have some person read out the textbooks or dictate the derivations in the lecture videos.

por Michael K

Oct 18, 2020

Lowest rating as the third course was absolutely poor. Low quality and in some way non-existent instruction.

por Nithin K

Jun 05, 2018

Too conceptual and theoretical making it difficult to understand. Examples would have helped a lot.

por Nabijonov K T

Jan 28, 2020

very very bad course! Assignments and quizzes made as shit. NO answers. Worth NOTHING!

por TUSHAR K

Jul 19, 2020

Previous Two Courses were better in terms of both assignments and teaching.

por Siddharth S

Jun 04, 2020

Very Poor when compared to previous two courses of this specialization.

por Saeif A

Jan 01, 2020

This course was a disaster for me. The first two were great though.

por Jared E

Aug 25, 2018

Impossible to do without apparently an indepth knowledge of python.

por Aditya P

Jul 04, 2020

Very poor teaching and overall it's the worst course I've taken

por Ahmed O M

Aug 27, 2020

Very bad explanation. The assignments need more instructions.

por Aurel N

Jul 05, 2020

k-NN assignment is full of errors and no proper explanations.

por Wensheng Z

Nov 24, 2019

Jumpy instruction with little illustrations

por Adam C

Oct 31, 2019

Worst course I've ever taken, online or IRL

por Zecheng W

Oct 20, 2019

Poorly organized and extremely confusing

por Mingzhe D

Dec 11, 2019

Assignment 1 cannot be passed!