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Avis et commentaires pour d'étudiants pour Mathematics for Machine Learning: PCA par Imperial College London

4.0
étoiles
1,813 évaluations
422 avis

À propos du cours

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....

Meilleurs avis

JS

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.

NS

Jun 19, 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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101 - 125 sur 422 Avis pour Mathematics for Machine Learning: PCA

par 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.

par 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.

par 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!!!!

par 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.

par 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.

par 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

par 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

par 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.

par 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

par 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.

par 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.

par 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!

par 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.

par 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.

par Rishabh P

Jun 17, 2020

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

par UMAR T

Mar 10, 2020

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

par Josef N

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.

par Dora J

Feb 04, 2019

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

par Vo T T

Sep 19, 2019

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

par Mukund M

May 24, 2020

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

par David H

Mar 21, 2019

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

par Lee F

Sep 28, 2018

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

par Nileshkumar R P

May 06, 2020

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

par Krzysztof

Aug 21, 2019

One of the most challenging course in my life - almost impossible without python and mathematics background.

par Sameen N

Sep 06, 2019

Amazing course and provides basic introduction for the PCA. Need for programming help in this course.