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Вернуться к Mathematics for Machine Learning: PCA

Отзывы учащихся о курсе Mathematics for Machine Learning: PCA от партнера Имперский колледж Лондона

Оценки: 2,319
Рецензии: 582

О курсе

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....

Лучшие рецензии

16 июля 2018 г.

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.

18 июня 2020 г.

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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151–175 из 577 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Sateesh K

24 сент. 2020 г.

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

автор: Moez B

24 нояб. 2019 г.

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

автор: Hasan A

30 дек. 2018 г.

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

автор: Duy P

24 сент. 2020 г.

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

автор: Alexander H

30 июля 2018 г.

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

автор: Prabal G

21 окт. 2020 г.

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

автор: Jason N

20 февр. 2020 г.

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

автор: Rishabh P

17 июня 2020 г.

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

автор: UMAR T

10 мар. 2020 г.

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

автор: Josef N

14 мая 2020 г.

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

автор: Dora J

3 февр. 2019 г.

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

автор: Trung T V

18 сент. 2019 г.

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

автор: Mukund M

24 мая 2020 г.

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

автор: David H

21 мар. 2019 г.

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

автор: Lee F

28 сент. 2018 г.

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

автор: Nileshkumar R P

6 мая 2020 г.

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

автор: Kuntal T

15 февр. 2021 г.

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

автор: Nishek S

30 июля 2020 г.

The PCA part Was a bit tricky barely handle the concepts.

thank you imperial team for such interactive course

автор: Krzysztof

21 авг. 2019 г.

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

автор: Pratama A A

25 авг. 2020 г.

Need more Effort to grasp the materials explained_-" you need to be patience,the lecturer is really on top

автор: Nelson S S

29 июля 2020 г.

Excellent course ... Quite challenging, a little difficult but I have learned a lot ... Thank you ...

автор: sameen n

6 сент. 2019 г.

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

автор: Brian H

24 февр. 2020 г.

Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.

автор: Natalya T

25 февр. 2019 г.

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

автор: Aishik R

18 янв. 2020 г.

Excellent and to-the-point explanations, useful assignments to make the concepts etched in memory