Chevron Left
Back to Mathematics for Machine Learning: PCA

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

4.0
stars
3,045 ratings

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

WS

Jul 6, 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

JS

Jul 16, 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.

Filter by:

151 - 175 of 758 Reviews for Mathematics for Machine Learning: PCA

By Ratnakar M

•

Jul 12, 2018

This is by far the best course I have taken. The Instructor is exceptional in setting the stage to understand the complex topic by letting us know the motivation of every concept, making us understand the fundamentals right, deep diving into the core of the topic and them nicely summarizing the topic along with the applications.

By MUHAMMAD Z I F

•

Mar 22, 2021

This course is amazing. But you if you guys maybe in the future to make some small example. I really dont get the concept when there is no example. I mean the example with a number in it or maybe i said the direct implementation. But all is great. Thanks you for teaching me this. I hope you guys well. thanks

By Geoffrey K

•

Jun 5, 2020

This course is at a higher level than the first two in the specialisation, and the instructor focusses on the mathematics of matrices, while the assessments are programming. There are easier courses for just PCA (which I thought helped me). Looks like most learners find a way through, and its worth it.

By Fernando G M G

•

Jun 30, 2020

It was a great course. Challenging at some points since I'm new in Python but it was worth the effort and I really learn a lot and now I comprehend the maths behind PCA algorithm. The point in which the relationship between eigenvalues of the covariance matrix is used in the PCA algorithm was amazing.

By Juan P M C

•

Sep 19, 2020

Even though I had lots of problems with the last coding exercise, I still learned a lot from this course. I loved how the instructor went from the basics of statistical representation and started using all of these tools in order to show us how the PCA algorithm works and why is it effective.

By Adithya P

•

Oct 1, 2020

Course 3 was quite challenging when compared to 1 and 2.

But, the instructor have explained the concept very well, the coding assignments were bit confusing and time killing.

Got to learn some important ML mathematics and the concept of projection, inner product and PCA were amazing.

Thank You

By surbhi

•

Jun 17, 2020

Learning Mathematics in this way and in efficient manner from basics and very clearly is really nice. I am very thankful to this course , teachers, Imperial College London as well as team of Coursera for providing such a great platform to learn all these skills and enhance our knowledge.

By David L

•

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.

By Training_Chotot

•

Jul 19, 2021

This is a good course coming with a very good book which you can use to reference later on even if you don't fully understand what or how PCA derives.

The exercise & lectures were interesting and guiding you enough to pass all tests. Take note and reference the book are keys to succeed.

By FRANCK R S

•

Jul 7, 2018

Very interesting and challenging subject: PSA, this MOOC together with the other 2 Mathematics for Machine Learning are one of the most useful I have ever made, actually they helped a lot in my other Machine learning and Deep learning studies! I highly recommend this fascinating MOOC

By mohit t

•

May 13, 2018

Perfect course. It takes up more time and effort than the other two courses in the specialization. But what you learn by the end of it is totally worth the effort. Note that this is an Intermediate course compared to the other two which are beginner. So the extra rigor is expected.

By Oj S

•

Jan 13, 2020

The introduction to PCA and steepest descent algorithms which might be a century old but still act the fundamentals of many state of art equations. So, you will learn the basics that how they function, and the real mathematics you need to know for ML using this course.

By Ashraf F

•

Oct 27, 2022

A really great course with mant well explained interesting concepts .. I personally liked the Python coding assignments in this course because it made me learn so much Python applied to very interesting problems .. Thanks for imperial college for such amazing work !!

By anurag

•

Apr 18, 2020

Its a very informational and interesting course. I understood a lot about PCA in this amazing course.

It was a good addition to the previous two courses of the certification. I would like to get similar courses in statistics and probability useful in Machine learning.

By Maksym B

•

Oct 18, 2020

Great course! It is a bit more challenging than the other courses in the specialization. It is great that this course is built based on two other previous courses. The lectures are great, the quizzes and programming assignments are complex enough to be interesting.

By Anna U

•

Jan 14, 2020

An excellently simple explanation of concepts of linear algebra and PCA. Applause for lector. I really liked this course and found it very useful for those newbies in machine learning like myself. I recommend this course to all my friends and others interested in.

By Umesh S

•

Dec 26, 2020

Most challenging of all three courses but rewarding as well. Requires you have refreshed complex topics of Linear Algebra ( Khan academy and other you tube material are good starting point) . Looking forward to go even deeper in to this. Thanks Imperial !!!

By Ramon M T

•

Oct 22, 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 Bingfeng H

•

Aug 26, 2020

Very good course, although the programming assignments are challenging and some background knowlege in linear algebra and vector calculus required. You will need to do some independent research at times. But the instructions are clear and concise.

By MELGAREJO E A

•

Jun 21, 2021

This course is of excellent quality. The teachers captured the knowledge perfectly in the MOOC. Although if you do not have knowledge in Python, it will be very difficult to successfully complete the course. Thank you Professor and Staff Coursera

By Xavier B S

•

Apr 5, 2018

Excellent course - challenging yet rewarding with good feedback from the teaching staff.

The video and the transparent white board are also great - look forward to seeing more MOOCs from Imperial as well as the release of the upcoming book

By Peter K

•

Dec 27, 2021

Better than the previous two courses in the spec. by one aspect: additional helpful materials are clearly pointed-out. Thanks Marc Peter Deisenroth for your effort. The book of Marc Peter Deisenroth is also recommended. Great course.

By Jafed E G

•

Jul 6, 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 Nur M H

•

Mar 24, 2023

This course is much more complicated than the previous two courses in the specialization. This one-of-a-kind course unravels the concepts and complex mathematics behind PCA. All in all, it was a pleasure to complete this course!

By Aisha J

•

Jun 16, 2022

It is not an easy course I needed to see the videos more than 1 time to understand, and taking the 2 courses before is significant to cope with this course. I thank instructor Marc Peter Deisenroth for teaching this course.