Вернуться к Mathematics for Machine Learning: PCA

4.0

Оценки: 590

•

Рецензии: 118

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....

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!

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Рецензии: 114

автор: Bryan Stafford

•Feb 19, 2019

This course needs a lot of work to get to the level of quality of its two predecessors.

автор: Avirup Guha

•Feb 18, 2019

Very poorly written/performed material with inadequate coding help. The engagement level is quite low. Will not recommend if you have novice programming background or new to math concepts.

автор: Prashant Dabholkar

•Feb 17, 2019

The lecturer is good and probably has a very good understanding of the mathematics. However if you are looking for a light and easy course, then this one is not for you. The mathematics is sometimes difficult to follow and although the lecturer patiently explains the derivation of the results, I had to go back and forth a number of times to understand what was happening.

автор: paulo

•Feb 11, 2019

great material but explanation are a little bit messy

автор: amit singh

•Feb 08, 2019

Unlike the prior courses in the series, topics not clearly explained and brought too sudden. Also none of calculations shown completely, instructor just wrote results in the end. Due to all these reason I was not able to finish the course.

автор: Lafite

•Feb 04, 2019

编程练习的质量不够高，不管是编程练习本身的代码逻辑、注释、练习的质量还是在答疑区课程组的答疑都不能尽如人意，对于编程练习并不很满意

автор: Dora Jambor

•Feb 04, 2019

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

автор: Patrick Frece

•Feb 01, 2019

The programming tasks are very bad documented and have errors.

автор: 용석 권

•Jan 30, 2019

Programming assignments' quality is too bad to follow it. Their lecture's explanation and assignments' notation are not matched. Moreover, the code is sometimes ridiculous.

автор: Manju Sharma

•Jan 29, 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.

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