Back to Mathematics for Machine Learning: PCA

4.0

stars

1,263 ratings

•

268 reviews

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!

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By Adrian C

•Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

By Jean D D S

•Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

By Wang Z

•Jul 08, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

By Iurii S

•Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

By NEHAL J

•Apr 21, 2019

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

By Ana P A

•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

By Chuwei L

•Apr 05, 2019

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

By Jyh1003040

•Jul 09, 2018

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

By Hsueh-han W

•Sep 20, 2019

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

By Gurudu S R

•Sep 16, 2019

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

By Sagun P S

•Mar 14, 2019

Tough one if you are new to programming or doesn't have excellent understanding of Maths

By Matan A

•Oct 20, 2019

The is a lot of gap from what the lecturer learn and what the assignments requires.

By Yuxuan W

•Oct 05, 2018

Always spending much more time on coding than needed. Same result but no credit :(

By Rafael C

•Dec 07, 2019

definitely one of the most catastrophic courses I've ever taken on Coursera...

By Meraldo A

•May 08, 2018

The course content was good; however, it was not well explained at times.

By Alexander

•Nov 06, 2019

Math for the sake of math. Too big jumps in calculations, too complex.

By k v k

•Nov 30, 2018

its a good course to learn mathematics essential for machine learning

By Rafael C

•Sep 24, 2019

The Classes didn't give the knowledge to solve the assignments.

By Shuyu Z

•Oct 18, 2019

The videos and instructions for the assignment are not clear.

By gaurav k

•Jul 03, 2019

More examples and visualization should be there to explain.

By Malcolm M

•Mar 06, 2019

Far more challenging than the first two courses.

By Sean W

•Nov 25, 2019

Notebook extremely buggy

By Tobias T

•Jul 14, 2019

If you like traditional lectures, which you go into a math class then feel puzzled, then go for it. Otherwise, the contents of this course are simply going through the mathematics equations and definitions, which can easily be found in textbooks. Ironically, the previous two courses in this specialization used lots of graphics and animations to help you understand the maths (either in terms of equation-wise or intuitively), this course completely lacks this element.

By Mark C

•Jul 31, 2018

Only on week 1 but this is already a disappointment compared to the first two classes in the Math for ML series which were excellent. Some content is presented too fast. Quiz questions are ambiguous. I already paid for the class so I will finish the content but not worry about passing quizzes and assignments. Had I known it would be like this I wouldn't have paid for it. Check out the other reviews and forum discussions to see what others think.

By Max B

•Aug 14, 2019

Pretty bad all around.

The teacher keeps throwing formulas without taking the time to explain why they are useful, and what they represent.

The first two courses were really good, and this one is a bummer.

Most of what I learned was learned elsewhere, the course acted as a detailed syllabus with some practice quiz (of relatively poor quality).

It's still worth taking if you completed the first two courses and want the specialization certification.