Chevron Left
Вернуться к Mathematics for Machine Learning: PCA

Mathematics for Machine Learning: PCA, Имперский колледж Лондона

4.0
Оценки: 686
Рецензии: 138

Об этом курсе

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....

Лучшие рецензии

автор: 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.

автор: 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!

Фильтр по:

Рецензии: 135

автор: 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

автор: NEHAL JOSHI

Apr 21, 2019

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

автор: 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.

автор: 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!!!

автор: Ajay Sharma

Apr 09, 2019

Great course for every one

автор: Chuwei Liu

Apr 05, 2019

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

автор: Yiqing Wang

Mar 28, 2019

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

автор: 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.

автор: 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.

автор: David Hwang

Mar 21, 2019

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