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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

4.0
звезд
Оценки: 2,276
Рецензии: 570

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

JS
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.

NS
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.

Фильтр по:

126–150 из 566 отзывов о курсе Mathematics for Machine Learning: PCA

автор: Dr. N D

12 авг. 2020 г.

It was a very nice experience with this course. I learnt a lot of Python Coding. The coding exercise was really good. It was tough for me to code in Python. But I took time for it. thanks to the faculty members.

автор: AKSHAT M

14 авг. 2020 г.

Really nice course and kudos to the instructor. Week 4 was a bit challenging, but still he made it quite easy for us to understand. Very happy to have gone through this course and completed the specialisation.

автор: Krishna K M

24 июня 2019 г.

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.

автор: Akshat S

24 июля 2019 г.

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!!

автор: Jose A

18 июля 2020 г.

Well explained, some issues with assignments but some of them are to not just type and think a little.

May be one is a real mistake... hard time with it, but lot of learning too.

автор: prudgin g

15 февр. 2020 г.

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.

автор: Christian H

28 дек. 2019 г.

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.

автор: Tse-Yu L

14 мар. 2018 г.

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.

автор: Miguel A Q H

20 февр. 2020 г.

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!!!!

автор: Aymeric N

25 нояб. 2018 г.

This course demystifies the Principal Components Analysis through practical implementation. It gives me solid foundations for learning further data science techniques.

автор: XL T

3 апр. 2020 г.

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.

автор: Fabrizio B

31 окт. 2020 г.

Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!

автор: S J

3 мая 2020 г.

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

автор: Christine D

14 апр. 2018 г.

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

автор: Ananta M

20 апр. 2020 г.

Although the course was little out there and the instructor was trying his best to articulate a difficult topic, the overall experience is great.

автор: Prime S

24 июня 2018 г.

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

автор: xiaoou w

21 нояб. 2020 г.

great content however the programming part is too challenging for people without propre guidance in the subject. the videos aren't of much help.

автор: J A M

21 мар. 2019 г.

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

автор: Amar n

11 дек. 2020 г.

Just Brilliant!!! Very well structured with very clear assignments. Doing the assignments is a must if you want to get clarity on the subject.

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