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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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201 - 225 of 758 Reviews for Mathematics for Machine Learning: PCA

By J A M

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Mar 21, 2019

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

By Amar n

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Dec 11, 2020

Just Brilliant!!! Very well structured with very clear assignments. Doing the assignments is a must if you want to get clarity on the subject.

By Sateesh K

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Sep 24, 2020

This course should be part of "gems of coursera". Excellent specialization, thoroughly enjoyed it. For me the 3rd course on PCA was the best.

By Moez B

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Nov 24, 2019

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

By Hasan A

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Dec 30, 2018

What a great opportunity this course offers to learn from the best in this simplified manner. Thank you Coursera and Imperial College London!

By Duy P

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Sep 24, 2020

Excellent explanation from the professor!! Besides he is the author of the book Mathematics for Machine Learning. You should check it out.

By Alexander H

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Jul 30, 2018

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

By Golnaz

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Oct 29, 2021

I liked how practical this course was. The programming assignments were really beneficial for a deeper understanding of the material.

By Prabal G

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Oct 21, 2020

great course for mathematics and machine learning...A big thanks to my faculty to guide like a god in this applied mathematics course

By Jason N

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Feb 20, 2020

A lot of reading beyond the video lectures was required for me and some explanations could be more clear. Overall, a great course.

By Rishabh P

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Jun 17, 2020

Well-detailed course and straight to the point. I enjoyed the course even though the programming assignments can be challenging

By UMAR T

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Mar 10, 2020

Excellent course it helps you understanding about linear algebra programming into real world examples by programming in python.

By Toan N

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Aug 19, 2023

The course is so good. Lectures are easy to understand, make some complicated problems become more simple and interpretable

By Giorgio B

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Mar 18, 2022

The leadup to PCA was needed and thought clear. I now have a better understand of how projections and inner products work.

By Josef N

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May 14, 2020

It would be great if the course is extended to 8 weeks, with the current week 4 spanning at least 3 weeks. Otherwise great.

By Teiichi A

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Aug 5, 2021

Challenging, with a lot to fill between the topics. Was shown how much further I can learn, which I am really grateful.

By Dora J

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Feb 3, 2019

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

By Azizbek O

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Mar 27, 2024

If you want to practice your knowledge which acquired in 1st and 2nd courses and coding skills just enroll this course!

By Trung T V

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Sep 18, 2019

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

By Mukund M

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May 24, 2020

Professor Deisenroth is amazing. Very tough course but appreciated all the derivations and explanations of concepts.

By David H

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Mar 21, 2019

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.

By Lee F

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Sep 28, 2018

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.

By Nileshkumar R P

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May 6, 2020

This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.

By Carlos J B A

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May 17, 2021

Undoubtedly one of the best courses I have taken on mathematics for Machine Learning with world-class teachers.

By Kuntal T

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Feb 15, 2021

one of the best course to learn whats happening in machine learning and how it make sense through mathematics.