Mathematics for Machine Learning: PCA(으)로 돌아가기

4.0
별점
2,818개의 평가
702개의 리뷰

## 강좌 소개

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....

## 최상위 리뷰

WS

2021년 7월 6일

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

2018년 7월 16일

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.

필터링 기준:

## Mathematics for Machine Learning: PCA의 700개 리뷰 중 201~225

교육 기관: Mukund M

2020년 5월 24일

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

교육 기관: David H

2019년 3월 21일

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

교육 기관: Lee F

2018년 9월 28일

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

교육 기관: Nileshkumar R P

2020년 5월 6일

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

교육 기관: Carlos J B A

2021년 5월 17일

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

교육 기관: Kuntal T

2021년 2월 15일

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

교육 기관: 037 N S

2020년 7월 30일

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

thank you imperial team for such interactive course

교육 기관: Krzysztof

2019년 8월 21일

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

교육 기관: Javier d V

2021년 6월 25일

Great course. An intermediate mathematical background is requiered. This is a strength in terms of learning

교육 기관: Pratama A A

2020년 8월 25일

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

교육 기관: Nelson S S

2020년 7월 29일

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

교육 기관: sameen n

2019년 9월 6일

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

교육 기관: Brian H

2020년 2월 24일

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

교육 기관: Natalya T

2019년 2월 25일

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

교육 기관: Aishik R

2020년 1월 18일

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

교육 기관: Haoquan F

2022년 2월 13일

It's overall wonderful but the week 4's programming assignment really struggled and confused me.

교육 기관: KAMASANI V R

2020년 6월 20일

This course helped me in getting a deeper knowledge on Principal Component Analysis. Thank You.

교육 기관: Wei X

2018년 10월 16일

concise and to the point. Might want to introduce a bit the technique of Lagrangin multiplier

교육 기관: Leonardo H T S

2021년 5월 2일

This was an amazing course, I really enjoyed it and learn a lot!

Thank you so much, greetings

교육 기관: Wahyu N A M

2021년 3월 27일

I'm struggle with assigments of week 4 about implementing PCA. But, yeaah finally i got this

교육 기관: Mayank

2020년 12월 3일

This course cleared so many concepts and enabled me to further master the subject on my own.

교육 기관: Ripple S

2020년 3월 17일

I learnt a lot from this course and now I think I am much more familiar with this algorithm.

교육 기관: Farhan F

2022년 3월 26일

T​his is very very very very very challengging, but i can do it because i try try and try

교육 기관: Haofei M

2020년 4월 22일

extremely informative and really help me understand the basic math in Machine learning

교육 기관: Deepak T

2020년 4월 17일

Course was challenging, so does the math. It was a very excellent learning experience!