返回到 Mathematics for Machine Learning: PCA

4.0

1,137 個評分

•

237 個審閱

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....

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.

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!

篩選依據：

創建者 Shounak D

•Sep 15, 2018

Great course !

創建者 Andrey

•Sep 17, 2018

Great course!

創建者 任杰文

•May 13, 2019

It's great, interesting and helpful.

創建者 Gautham T

•Jun 16, 2019

excellent course by imperial

創建者 Sriram R

•Jun 18, 2019

This is one of toughest course in this specialization. Having said that, it was interesting to learn about the inner working of the PCA and is well taught. At times it was tough to follow and could have been better if there are some additional examples explained to reinforce the concept. Also week 4 is kind of rushed with little or no time to fully appreciate the beauty of PCA.

創建者 Rishabh A

•Jun 17, 2019

We need more elaborate explanation at few tricky places during the course.

創建者 Hritik K S

•Jun 20, 2019

Maths is just like knowing myself very well!

創建者 Abhishek M

•Jun 22, 2019

Very nice course. It will be great to have a course on Statistics for Machine learning covering advanced concepts in probability theory. Thank you for offering such a great course. I have learnt a lot and enjoyed fully.

創建者 Krishna K M

•Jun 24, 2019

I am not sure why the rating is so low for this course.

Personally, I found this course really insightful as the instructor explains what the different statistical measurements mean, and why are they useful.

創建者 Jafed E

•Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

創建者 Lia L

•May 22, 2019

This was really difficoult, but I'm so proud for the completion of the course.

創建者 Gergo G

•May 15, 2019

This course is really challenging. A strong mathematical background is necessary or it needs to be developed during the lectures and self-study. The professor's explanations are clear, and still lead to complex ideas which is great. Programming assignments are also difficult, however they serve as a superb opportunity to develop your skills in Python.

創建者 David

•May 29, 2019

This was indeed a very challenging course. It was also very rewarding, and I felt that the instruction was great and relevant to the assigned tasks. The first two courses in the specialization were very high quality, and in my opinion this one lives up to the expectations that they set.

創建者 Arnab M

•Jun 03, 2019

A great course. Learnt a lot, a lot of Linear Algebra, Projections/ Geometry/ all of these Mathematical ideas would help greatly in understanding of Machine Learning concepts and applying them to real world data!!..

創建者 Sujeet B

•Jul 21, 2019

Tough, but great!

創建者 David N

•Jul 24, 2019

Great course

創建者 Akshat S

•Jul 24, 2019

I will present my self with some amazing songs!!

Excellent staircase to the heaven for learning PCA.

Breaking the habit of struggling with hardcore bookish mathematics.

Loose yourself in this adventure!!

創建者 Greg E

•Jul 27, 2019

I have thoroughly enjoyed every course of this specialization. Thank you very much.

創建者 Samresh

•Aug 10, 2019

Nice Course.

創建者 Mohamed H

•Aug 10, 2019

fantastic

創建者 Krzysztof

•Aug 21, 2019

One of the most challenging course in my life - almost impossible without python and mathematics background.

創建者 Keisuke F

•Sep 15, 2019

I had big fun of PCA

創建者 Shahriyar R

•Sep 14, 2019

The hardest one but still useful, very informative neat concepts

創建者 Lahiru D

•Sep 16, 2019

Great course. Assignments are tough and challenging.

創建者 María J S G

•Aug 29, 2019

Very good 3 courses for those of us who are beginners in Machine Learning and IA! However I miss a whole course, perhaps the first one of then four, teaching us what we need to know about python, numpy and plotting.