Chevron Left
Back to Mathematics for Machine Learning: PCA

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

4.0
1,187 ratings
252 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!

Filter by:

101 - 125 of 249 Reviews for Mathematics for Machine Learning: PCA

By Lia L

May 22, 2019

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

By Yuanfang

Sep 07, 2019

A little more challenging than the other 2 courses in this series. The programming examples on K nearest neighbors, eigenvector fitting of facial data, and the PCA implementation are neat and rewarding. Can't help but feel there's still a great deal of math details that is only briefly mentioned - oh well there's always the free textbook to reference. Overall highly recommended.

By Oleg B

Jan 06, 2019

Excellent focus on important topics that lead up to PCA

By 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.

By 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.

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 Tichakunda

Jan 18, 2019

good course, rigorous proof and practical exercises

By Natalya T

Feb 25, 2019

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

By Keisuke F

Sep 15, 2019

I had big fun of PCA

By Andrey

Sep 17, 2018

Great course!

By Mohamed H

Aug 10, 2019

fantastic

By Ajay S

Apr 09, 2019

Great course for every one

By Lintao D

Sep 24, 2019

Very Good Course

By Alexander H

Jul 31, 2018

Highly informative course! Loved the depth of the material. Found this course content highly useful in my current project based on PCA.

By Wei X

Oct 16, 2018

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

By Mohammad A M

Nov 14, 2019

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

By 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

By Lafite

Feb 04, 2019

编程练习的质量不够高,不管是编程练习本身的代码逻辑、注释、练习的质量还是在答疑区课程组的答疑都不能尽如人意,对于编程练习并不很满意

By Ronald T B

Jan 21, 2019

it is very challenging course, of course you will complain at first on how lack the programming explanation is given. However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.

By Mark R

Jan 22, 2019

Good, short, overview of PCA

By paulo

Feb 11, 2019

great material but explanation are a little bit messy

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 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 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.