RP
18 мая 2019 г.
This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.\n\nA big thank you for spending so many hours creating this course.
NN
14 окт. 2016 г.
It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.
автор: Hamed B
•5 июня 2019 г.
THE BEST COURSE IN ML BY FARRRRRRRR
автор: Bhanu p T
•1 янв. 2019 г.
Loved it. Easy and Excellent Course
автор: Lưu V L
•5 авг. 2020 г.
best ML course in the world !!
автор: Jaspinder S V
•8 авг. 2015 г.
Awesome course for beginners.
автор: Mulat Y
•14 февр. 2020 г.
Machine Learning
Data Science
автор: Mewada A J
•5 авг. 2020 г.
best experience of learning
автор: 梁驰
•8 февр. 2020 г.
喜欢吴恩达教授的课,讲的非常的好!教授很谦虚!赞赞赞!
автор: chandan k
•6 июня 2019 г.
Great course to study!
автор: Eugene M
•4 янв. 2019 г.
Very useful course!
автор: Joy F Y
•7 авг. 2015 г.
It's very useful
автор: WANG B
•8 авг. 2021 г.
Andrew Ng yyds!
автор: Pavel K
•6 июня 2019 г.
A great course.
автор: Hacker O
•17 июня 2019 г.
very good!!
автор: Stephen M
•5 июня 2019 г.
Very useful
автор: ylfgd
•6 июня 2019 г.
very good
автор: Thierry L
•4 янв. 2019 г.
Excellent
автор: Saiful A
•7 авг. 2015 г.
Very Nice
автор: Vivek K
•13 дек. 2018 г.
Awsome
автор: Nazir A Z
•29 июля 2021 г.
great
автор: Lichen N
•28 авг. 2019 г.
深入浅出
автор: Sam C
•2 янв. 2020 г.
I'm not crazy about online learning. There are certain aspects of classroom learning that online learning can't give. But as far as online learning goes, this course is probably about as good as it ever gets.
Prof. Ng gives very clear expositions of the fundamentals of machine learning. Anyone taking this class and completing the assignments will be ready to apply machine learning to at least some simpler real world problems and should be in a position to quickly pick up more advanced techniques for more complex problems.
The exams are fair (although I think some more work could have been done to make many of the questions less ambiguous). The programming assignments can be a time sink, but I don't think they could have been any shorter and still give valuable practice in using the techniques outlined in the lectures.
Students who already have a background in linear algebra or the basics of data analysis might find the pace of the class in the early units, where Prof. Ng deals with linear regression, to be rather slow. But if you can get through those early units, you will definitely find yourself dealing with new material (and occasionally appreciating the initial slow pace).
Octave/Matlab is the only language in which the assignments are accepted. I personally would have voted for python. But Prof. Ng spends a few lectures telling you all you need to know about Octave/Matlab, for the purposes of the course. (To save time, I would advise that you spend a day or two learning the language on your own before starting this course. That will allow you to stay that much more ahead of the due dates. But maybe that's just me.)
One word of warning is that, as a friend of mine said after taking a machine learning class in a traditional university classroom, this material makes machine learning accessible, but also takes the "magic" out of it. If you are impressed at how Netflix can be so good at recommending new movies for you to watch, well, after taking this class, you won't be impressed anymore. You'll probably be figuring that, yeah, they probably have some tricks I don't know about, but I could do 90% of what they're doing myself! Which actually means it's a good class!
One thing I definitely would have added are some words at the end of the course about what the "hot topics" are in machine learning, and suggestions about where to go from here, what topics would reward further study, and what books, websites etc. are available for studying them. For example, some words on where to study how and when machine learning turns into full blown artificial intelligence would be appreciated.
The only real gripe I have is that the assignment due dates really didn't give appropriate regard to how busy real life can get during the winter holidays. After all, the big selling point of online learning is flexibility! Right?
In summary: I figure this class is about as good as online learning will get. The instructor is very clear; the assignments are fair and useful. I would have done a few things differently, but nothing is ever perfect. This is a good class for anyone wanting to know the basics of machine learning. Four stars.
автор: Saideep G
•9 апр. 2019 г.
Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.
автор: Cheung C H
•8 дек. 2021 г.
1. better teach in Python
2. sound recording quality can be better
3. Overall content is good
4. please provide more math details, such as in back propagation, and partial derivative is actually very basic where every undergraduate should already know
автор: Doreen B
•9 июня 2019 г.
Well explained, at the end of this course you will understand the subject and hold coherent conversations about it. Matlab implementation relatively simple, maybe too much so. Highly recommended course.
автор: José M V G
•9 февр. 2022 г.
Great course for grasping the fundamental algorithms of Machine Learning, even though the assignments are quite outdated in my opinion and don't align with the state of the art in programming.