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Learner Reviews & Feedback for Mathematics for Machine Learning: PCA by Imperial College London

4.0
1,180 ratings
251 reviews

About the Course

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....

Top reviews

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.

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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101 - 125 of 248 Reviews for Mathematics for Machine Learning: PCA

By Keisuke F

Sep 15, 2019

I had big fun of PCA

By Lahiru D

Sep 16, 2019

Great course. Assignments are tough and challenging.

By Shahriyar R

Sep 14, 2019

The hardest one but still useful, very informative neat concepts

By Gautham T

Jun 16, 2019

excellent course by imperial

By Vo T T

Sep 19, 2019

This course is very helpful for me to understand Math for ML. Thank you Professors at Imperial College London so much!

By Faruk Y

Sep 22, 2019

Lectures and programming assignments were selected nicely to teach the math of PCA

By Lintao D

Sep 24, 2019

Very Good Course

By Alfonso J

Oct 20, 2019

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

By Wang S

Oct 21, 2019

A little bit difficult but helpful, thank you!

By Idris R

Nov 02, 2019

Great, challenging course. The instructor will expect much of you as the material is not spoon fed. At times this is frustrating but yet that's the best way to build your own intuition. This is a *hard* course and I imagine most of machine learning is like this. Fun, rewarding, and challenging. You'll flex your math and programming muscles.

By Arijit B

Nov 05, 2019

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

By Ramon M T

Oct 23, 2019

I liked the course quite a bit. I found it quite challenging (I had never seen any PCA) but it always kept me very interested. I had to use several sources to read a little more about PCA and to complete the last exercises, the forum is very helpful.

By Mohammad A M

Nov 14, 2019

This course is also so helpful, and the lecturer is so predominant on what he taught.

By Roshan C

Nov 23, 2019

the course was very much intuitive and helpful to grasp the knowledge of PCA

By Harish S

Nov 24, 2019

This was a difficult course but still very informative.

By Moez B

Nov 25, 2019

Excellent course. The fourth week material is the hardest for folks not comfortable with linear algebra and vectorization in numpy and scipy.

By Laszlo C

Dec 06, 2019

This is an excellent course first covers statistics, looks back to inner products and projections, thereafter it connects all of that and introduces PCA. The knowledge that you've gathered throughout the first two courses gets applied here. Granted, it's more abstract and challenging than the others, I wouldn't give a worse rating just because of that. You'll need to dive into certain topics on your own and if you strengthen your coding skills for the programming exercises. Nevertheless, it's just as highly rewarding as the first two.

By Mark R

Jan 22, 2019

Good, short, overview of PCA

By Camilo J

Mar 01, 2019

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

By Changxin W

Jan 28, 2019

Many errors of homework

By paulo

Feb 11, 2019

great material but explanation are a little bit messy

By Shraavan S

Mar 04, 2019

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

By Thorben S

Mar 08, 2019

I would have liked to be introduced to the topic on a higher level first - and then, step by step, an introduction of the math to solve specific problems in the progress. That would be a perfect approach, especially for data scientists who just want to understand the underlying math for such a widely used technique.

By Jonathan F

Mar 17, 2019

This course is way harder than the first two. The maths itself is more difficult. The Python parts are a lot more challenging because they require a good understanding of the way Numpy handles vectors and matrices. But the end result is good and it is worthwhile!

By Cesar A P C J

Dec 23, 2018

Good content, just need to fix the assignments' platform.