返回到 Mathematics for Machine Learning: PCA

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138 个审阅

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....

创建者 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.

创建者 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!

筛选依据：

135 个审阅

创建者 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

创建者 NEHAL JOSHI

•Apr 21, 2019

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

创建者 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.

创建者 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!!!

创建者 Ajay Sharma

•Apr 09, 2019

Great course for every one

创建者 Chuwei Liu

•Apr 05, 2019

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

创建者 Yiqing Wang

•Mar 28, 2019

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

创建者 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.

创建者 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.

创建者 David Hwang

•Mar 21, 2019

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.