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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.

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176 - 200 of 758 Reviews for Mathematics for Machine Learning: PCA

By chaomenghsuan

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Jul 18, 2018

This one is harder, I took longer time to figure out the assignments. Some of the concept that appeared in the assignments were not included in the lectures. I do hope that the assignments could have clearer instructions.

By Abhishek M

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Jun 21, 2019

Very nice course. It will be great to have a course on Statistics for Machine learning covering advanced concepts in probability theory. Thank you for offering such a great course. I have learnt a lot and enjoyed fully.

By Mjesus S

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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 Arnab M

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Jun 3, 2019

A great course. Learnt a lot, a lot of Linear Algebra, Projections/ Geometry/ all of these Mathematical ideas would help greatly in understanding of Machine Learning concepts and applying them to real world data!!..

By Dr. N D

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Aug 12, 2020

It was a very nice experience with this course. I learnt a lot of Python Coding. The coding exercise was really good. It was tough for me to code in Python. But I took time for it. thanks to the faculty members.

By AKSHAT M

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Aug 14, 2020

Really nice course and kudos to the instructor. Week 4 was a bit challenging, but still he made it quite easy for us to understand. Very happy to have gone through this course and completed the specialisation.

By Krishna K M

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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 Akshat

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Jul 24, 2019

I will present my self with some amazing songs!!

Excellent staircase to the heaven for learning PCA.

Breaking the habit of struggling with hardcore bookish mathematics.

Loose yourself in this adventure!!

By Jose A

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Jul 18, 2020

Well explained, some issues with assignments but some of them are to not just type and think a little.

May be one is a real mistake... hard time with it, but lot of learning too.

By prudgin g

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Feb 15, 2020

Challenging, but doable. Has some bugs in coding assignments, but clearing them out makes you understand things better. Get ready to spend extra time understanding the concepts.

By Shreyas G

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Sep 18, 2021

Very challenging course, requires intermediate knowledge of Python and the numpy library. PCA week 4 lab was truly a mind-blowing experience, taking over 5 hours to complete.

By Christian H

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Dec 28, 2019

This course is well worth the time. I have a better understanding of one of the most foundational and biologically plausible machine learning algorithms used today! Love it.

By Tse-Yu L

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Mar 14, 2018

Practices and quiz are designed well while I will suggest to put more hints on programming parts, e.g., PCA. Overall, this series of course are pretty useful for beginner.

By Miguel A Q H

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Feb 20, 2020

This is the best course of the specialization, its very hard but it lets you to understand very important concepts of what means dimensionality reduccion.

Great Job!!!!

By Aymeric N

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Nov 25, 2018

This course demystifies the Principal Components Analysis through practical implementation. It gives me solid foundations for learning further data science techniques.

By XL T

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Apr 3, 2020

It is a bit difficult and jumpy. You will need some hard work to fill in the missing links of knowledge which not explicite on the lectrue. Overall, great experience.

By Camilo V F

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Jul 20, 2022

Really clear and well explained. The concepts are treated in detail enough to be applied. Very happy to have invested my time in this course. I strongly recomend it.

By Fabrizio B

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Oct 31, 2020

Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!

By Prut S

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Aug 16, 2021

The content was challenging but very well structured. It is nice to understand the mathematics behind it rather than just blindly using PCA in your projects.

By S J

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May 3, 2020

Your Teaching and Video quality is par excellence.....Thanks a lot for such amazing stuff...I am looking forward to joining more courses in the same line

By Duc H B

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Dec 16, 2021

I think it is the best hard in 3 course of the series, but It give many new knowlegde and build a mindset with math for machine learning.

Great Course!

By Christine D

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Apr 14, 2018

I found this course really excellent. Very clear explanations with very hepful illustrations.

I was looking for course on PCA, thank you for this one

By Ananta M

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Apr 20, 2020

Although the course was little out there and the instructor was trying his best to articulate a difficult topic, the overall experience is great.

By Prime S

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Jun 24, 2018

Nicely explained. Could be further improved by adding some noted or sources of derivation of some expressions, like references to matrix calculus

By Xiaoou W

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Nov 21, 2020

great content however the programming part is too challenging for people without propre guidance in the subject. the videos aren't of much help.