Back to Mathematics for Machine Learning: PCA

4.0

stars

1,324 ratings

•

289 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 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 Reinaldo L N

•Feb 27, 2020

Last assignment was hell on Earth...

By Sean W

•Nov 25, 2019

Notebook extremely buggy

By Erik P

•Feb 12, 2020

The first two courses in this series are excellent. However, this third course is taught by a new teacher and this introduces a remarkable drop in quality.

There are of cause different styles of teaching. However, as a minimum a teacher should strive towards conveing to students the importance of the subject at hand and the intuition behind it. However, this teacher settles for monotonously writing out formulas and definitions that can simply be read in the course formula PDF. Thus, watching the videos becomes a waste of time. In turn, this makes it harder to complete quizzes and assignments since one first has to go searching the internet for web pages that actually explain rather than simply state formulas that one needs to combine and apply in order to solve the assignments.

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.

By Nouran G

•Oct 11, 2018

Course is inconsiderate to new learners in that new concepts were very sloppily introduced. Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best. Unlike the other two courses, it assumes python knowledge, doesn't explain relevant syntax in the assignments; which made me take a lot of long unnecessary detours to get the python implementation right.

By Marvin P

•Apr 24, 2018

After the other two awesome courses of the specialization this one stays far behind my expectations. Weakest course of the specialization. Instructor is obviously knowledgeable but does not provide much intuition. Programming assignments are really difficult and at many points frustrating. 2 more weeks and therefore comprehensive instructions would be desirable. Couldn't appreciate that course as much as I wanted to.

By Michalis D

•Jul 22, 2019

After having done the first two parts of the specialization, I am afraid this one didn't stand up to the high quality bar the previous two had set. The programming assignments are unnecessarily long and complex and the overall material is not as engaging, connected and concise. I might give it a good rating as a standalone but now I can't avoid comparing it to the other two parts of the specialization.

By Daniel U

•Sep 27, 2018

Programming assignments seemed to be written from a completely different direction, and instructions are vague and misleading. (The math assignments were not so bad.) There was no staff or mrntor engagement in the forums during the period of the course.

By amit s

•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.

By Kevin L

•Sep 11, 2018

The course assignments could be improved dramatically, though the course itself has very good content if you want to have a taste of how linear algebra (predominantly) can be implemented to solve machine learning problems.

By shashank s

•Feb 17, 2020

First two courses in this series are great but not this one. Lectures and exercises are not related. I do not feel like I have totally understood PCA. Was able to complete the final assignment thanks to the internet.

By Ivo R

•Nov 16, 2019

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

By Vignesh N M

•Sep 12, 2018

Explaination of many things are skipped, assumption was made by the instructor that lot of things were already known by the learner. It could have been much better.

By Martin H

•Dec 08, 2019

Lack of examples to clarify abstract concepts. Big contrast in quality compared to the other courses in this specialization.

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