Back to Mathematics for Machine Learning: PCA

4.0

stars

1,629 ratings

•

366 reviews

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....

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.

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:

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 Xavier B S

•Apr 05, 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 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 chaomenghsuan

•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

•Jun 22, 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 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 Arnab M

•Jun 03, 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 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 Akshat S

•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 Aleksey A

•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 Christian H

•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

•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 Q

•Feb 21, 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

•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

•Apr 04, 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 S J

•May 03, 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 Christine D

•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

•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

•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 J A M

•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 Moez B

•Nov 25, 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

•Dec 31, 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 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 Jason N

•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 UMAR T

•Mar 10, 2020

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

- AI for Everyone
- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
- Site Reliability Engineering
- Speak English Professionally
- The Science of Well Being
- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
- Foundations of Everyday Leadership

- Deep Learning
- Python for Everybody
- Data Science
- Applied Data Science with Python
- Business Foundations
- Architecting with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Excel to MySQL
- Advanced Machine Learning
- Mathematics for Machine Learning
- Self-Driving Cars
- Blockchain Revolution for the Enterprise
- Business Analytics
- Excel Skills for Business
- Digital Marketing
- Statistical Analysis with R for Public Health
- Fundamentals of Immunology
- Anatomy
- Managing Innovation and Design Thinking
- Foundations of Positive Psychology