Voltar para Mathematics for Machine Learning: PCA

4.0

690 classificações

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139 avaliações

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....

por 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.

por JV

•May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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136 avaliações

por Nelson Fleig Aponte

•Apr 25, 2019

This course brings together many of the concepts from the first two courses of the specialization. If you worked through them already, then this course is a must. There are some issues with the programming assignments and the lectures could do with some more practical examples. Be sure to check the discussions forums for help. For me they were essential to passing the course.

por Ana Paula Appel

•Apr 22, 2019

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

por NEHAL JOSHI

•Apr 21, 2019

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

por Yana Khalitova

•Apr 18, 2019

Not really well structured. Too much in-depth details, too little intuition given. Didn't help to understand PCA. Had to constantly look for other resources online. Pity, because first 2 courses in the specialisation were really good.

por Cécile Logé

•Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

por Ajay Sharma

•Apr 09, 2019

Great course for every one

por Chuwei Liu

•Apr 05, 2019

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.

por Yiqing Wang

•Mar 28, 2019

The teaching is good but some programming assignment is not so good

por Ткаченко Вячеслав Евгеньевич

•Mar 24, 2019

Algebra course is excellent. Calculus course is good. PCA is so bad that I am still upset that I spent my time on it.

por J A Marin

•Mar 21, 2019

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

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