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

4.0
stars
2,601 ratings
646 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 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.

NS
Jun 18, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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301 - 325 of 641 Reviews for Mathematics for Machine Learning: PCA

By Mellania P S

Mar 23, 2021

great

By Indah D S

Mar 9, 2021

great

By Md. R Q S

Aug 21, 2020

great

By Agung W

Mar 28, 2021

nice

By Ahmad H N

Mar 20, 2021

Good

By GEETHA P

Jul 28, 2020

good

By RAGHUVEER S D

Jul 25, 2020

good

By Harsh D

Jun 28, 2018

good

By Mochammad G R M

Mar 25, 2021

@.@

By Roberto

Mar 26, 2021

gg

By ahmed b

Apr 15, 2021

a

By Wasif S

Aug 23, 2020

I want to make this more of a guideline rather than a direct catch & read Review because of the nature of this course. But first, congratulations to all who have managed to pass this course. Now the big discussion. If you have taken the enrollment prior to the other courses under the specialization, then you have several decisions to make. First of all, this course requires HIGH PATIENCE & good HOMEWORK times. This course is also HIGH on programming. So, if you are not familiar with Numpy, then you have to put more PATIENCE than before. Thereby, if you are a newbie in Numpy & up for the challenge to learn the steps & then implement on the code, you should consider enrolling in this course. Those who lack in PATIENCE & code-correcting scenarios, should not enroll in this. I am not going to rate this course (although, without putting stars I cannot submit this writing). Why? This is a 5-star course if you judge the difficulty & advanced topics covered throughout. This is a 4-star course if you seem to find your linear algebra knowledge start to tumble sometimes & the coding assignments are up for the game with lack of clarity. This is a 3-star course because of the Instructor's approach to explaining the abstractness of the higher dimensions. If you go more abstract in already more abstract things, that is more like adding salt to the wound. This is a 2-star course if you all on a sudden realize that the entire knowledgebase around Linear Algebra is falling apart & (AND) the coding assignments are feeling like a living mystery, especially the instructions may sound more confusing. This course is not a 1-star & if anyone rates it a 1-star that is because he/she is a sore loser. Nothing goes without effort. The whole team definitely put effort to cover the complexity and balance in between. But they weren't quite successful. If you up for a challenge, you are welcome to get into it. If you are hesitant, have some ice-cream & try later. Thanks.

By Ertuğrul G

Jun 7, 2020

The overall experience was very good. I have enjoyed all the math in videos and PCA derivation throughout the course. The course a bit harder than the previous ones in the specialization. However after some effort one can understand the points that is not taught thoroughly. Only downside of the course is the programming environment. I have attended different courses that are also using Jupyter notebooks on Coursera and they were flawless. Here we have, some cells do run forever, a grader behaving inconsistently and one week that has some steps completely against the general software engineering principles. By the way discussion forums are so helpful and make me understand some math concepts on the way. I recommend the course to people who want to improve their understanding of math before deep diving machine learning courses.

By Niju M N

Apr 9, 2020

This is the final course in the Specialization, that focuses on Principal component Analysis.This course is a bit hard compared to the other two courses in specialization. This builds on the topics explained in the other two courses.The Instructor tries to squeeze the concepts in the limited time.Not all materials are completely explained in the video, however, students can refer to other materials available in the web/ Refer the course forums and get the concepts and use them to solve the Quizzes. Some times the Assignments and quizzes are frustrating , however they do a good job of reinforcing the ideas taught in the video. Totally this is a good time spent .

By Vassiliy T

Jul 10, 2018

it is good, challenging course. i've learned a lot, but feel that i came away with quite patchy knowledge. This course is a big step up in complexity and delivery form the previous two courses. perhaps my expectations were not right to start with - one cannot learn this level of complexity so quickly. Admittedly there are many gaps between the lectures and course materials and what is asked in programming assignments. i ended up reading a lot online to fill in the gaps (i've learned a lot of python during the course, which is great!).nevertheless, after this course i feel equipped to continue with machine learning.

By Matteo L

Apr 20, 2020

I think this course is slightly underrated at the moment. The topic is not an easy one and I thought the teacher did a great job of explaining it as clearly as possible using an appropriate amount of mathematical derivation.

I really thought the last week of the course was great, especially considering that everything we had seen so far in the specialization was used to develop the PCA algorithm. It's quite amazing how topics such as eigenvectors, projections and optimization all come together here.

I think the notebooks were quite challenging compared to the previous two courses with is definitely a plus!

By Aileen F

Dec 14, 2020

It's a lot harder compared to the earlier courses in the specialization. Video lessons focus more on the theory and lack the visualization and practice problems of the previous courses. Some of the programming assignments can still be polished by including the discussion in between the codeblocks like the assignments in the previous course. Assertion errors in the notebook do not always reflect possible assertion errors in the grader. The difficulty reminds me of doing my own research and debugging my codes during college and those are useful lessons in graduate school and life.

By Nikolay B

Aug 3, 2019

Instructor gives the very dry but useful essence of the "philosophical" concepts of dot and generalized inner product, etc., - personally, liked that. Unfortunately, the offered problems are so far away from the delivered videos but the web search helps on getting the hints. This course makes you think - I learned a lot just by asking myself "what do they mean under this statement?", what they want in this task? Though I will appreciate if providers elaborate the material further and so instead of googling we spend our time watching - a single point access.

By Raul B M

Nov 1, 2020

There were more typographical errors in this course compared to the first 2. In general, it was more challenging, but not in a good way. Often times I had to resort to looking at the discussions in the forums, and I simply wouldn't have been able to finish it without doing so. Unlike in the first 2 courses where I could spend a long time doing the exercises, but I could figure it out from my own notes. There also seemed to be some problems with the autograders of the Jupyter notebooks that the staff may want to review. Still, I learned a lot. Thanks team!

By Luke L

Jun 11, 2020

This course was a very interesting end to the specialization. The first two courses teach you the tools necessary, while this course teaches practical (although highly theoretical) application of those tools. The reason for the non-five star rating is the python exercises take one on quite a journey, which often times goes through some dark and murky tunnels which are tough to escape. Also, there are few worked examples during lectures.

By Claudio P

Jan 24, 2020

It was a real tour de force on the mathematics, and I had some hard time following the ideas of the instructor many times. However, the topic was completely covered in a very systematic way, which is excellent in my opinion. My only suggestion is to focus more on what really matters: do we really have to spend such a long time discussing about different metrics for an inner product if in the end we only use the euclidean metric?

By Rob O

Aug 3, 2020

An in-depth exploration of the PCA algorithm and the math behind it. Python programming exercises helped me to solidify the theory and derivations. Numpy is used extensively in the exercises and I liked the experience that I gained in applying it. I found that the connection between the exercises and theory were not always clearly drawn and needed to refer to the discussion to fill in the gaps.

By Barnaby D

Jan 3, 2020

Would give this course 5 stars if it was properly described so that expectation could match reality:

Give yourself plenty of time for this course - it will take quite a bit longer than described.

Make sure you are comfortable with Python and NumPy before you start (particularly the linear algebra functions).

It is very different (much less hand-holding) than the other courses in the specialization.

By Jorge G

Sep 15, 2020

its a good course, some exercices are not for beginner programers. I think on the PCA chapter the projection matrix is described wrong, I used the formula from previous weeks and it worked. I think the relation between kmeans and pca is not explained only on a programming task is breafly discussed but its described out of nowhere so you have to read the code to understand whats going on.

By Nelson F A

Apr 25, 2019

This course brings together many of the concepts from the first two courses of the specialization. If you worked through them already, then this course is a must. There are some issues with the programming assignments and the lectures could do with some more practical examples. Be sure to check the discussions forums for help. For me they were essential to passing the course.