Volver a Mathematics for Machine Learning: PCA

4.0

455 calificaciones

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93 revisiones

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 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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89 revisiones

por k vinay kumar

•Nov 30, 2018

its a good course to learn mathematics essential for machine learning

por Aymeric Nguyen

•Nov 25, 2018

This course demystifies the Principal Components Analysis through practical implementation. It gives me solid foundations for learning further data science techniques.

por Brock Imel

•Nov 21, 2018

Way too hard compared to the other courses in the specialization. I feel like I wasted my money on this.

por Andrei

•Nov 01, 2018

terrible assignments

por José Delfosse

•Oct 31, 2018

This course is harder that the the two first ones. You have to do a lot more by yourself. There will be some frustrations with assignments that are not always easy or clear, with confusing python/numpy notations not really introduced during the course. Also, most assignment didn't work online, so I had to install python3 and jupyter to work on them locally and submit them manually. You should expect to spend more time than announced. All in all, I've learned new things and that's the most important. I believe there are room for improvement for this course.

por Jichen Wu

•Oct 27, 2018

Explanation of course material is not clear

por sreekar

•Oct 23, 2018

The instruction is absolutely bad and not worth it. However, if you have patience to re-watch, refer to other supporting materials, learn on your own a lot and then have patience to deal with programming asssignments ,...then you might find the final result useful.

por Martin Belder

•Oct 22, 2018

Overall: worthwhile content, but poor execution. Especially assignments need improvement.

Good points:

-The contents tend to be worthwhile.

-The instructor is thorough and clear.

Bad Points :

-To those who are not as familiar with mathematical terminology the instructor is a tough act to follow sometimes.

-The great disappointment of this course lies in the assignments. They don't really add to my understanding of the mathematics involved, and are quite often a distraction because the assignments are quite inflexible in terms of coding: you'll have to stick quite close to what the instructor envisions, or you will fail. This is especially frustrating because you will have a hard time figuring out whether you failed because your code was faulty or because your conceptual understanding was faulty.

por Nguyen Duy Phuong

•Oct 17, 2018

That's a great online courses can help people have enough background to break into Machine Learning or Data science

por Wei Xie

•Oct 16, 2018

concise and to the point. Might want to introduce a bit the technique of Lagrangin multiplier

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