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Volver a Mathematics for Machine Learning: PCA

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

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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....

Principales reseñas

WS

6 de jul. de 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

16 de jul. de 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 de 695 revisiones para Mathematics for Machine Learning: PCA

por S J

3 de may. de 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

por Bui V H D

16 de dic. de 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!

por Christine D

14 de abr. de 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

por Ananta M

20 de abr. de 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.

por qwer q

24 de jun. de 2018

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

por Xiaoou W

21 de nov. de 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.

por J A M

21 de mar. de 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.

por Amar n

11 de dic. de 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.

por Sateesh K

24 de sep. de 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.

por Moez B

24 de nov. de 2019

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

por Hasan A

30 de dic. de 2018

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

por Duy P

24 de sep. de 2020

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

por Alexander H

30 de jul. de 2018

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

por Golnaz

29 de oct. de 2021

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

por Prabal G

21 de oct. de 2020

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

por Jason N

20 de feb. de 2020

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

por Rishabh P

17 de jun. de 2020

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

por UMAR T

10 de mar. de 2020

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

por Giorgio B

18 de mar. de 2022

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

por Josef N

14 de may. de 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.

por Teiichi A

5 de ago. de 2021

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

por Dora J

3 de feb. de 2019

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

por Trung T V

18 de sep. de 2019

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

por Mukund M

24 de may. de 2020

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

por David H

21 de mar. de 2019

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