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學生對 斯坦福大学 提供的 机器学习 的評價和反饋

4.9
121,674 個評分
29,878 個審閱

課程概述

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

熱門審閱

RR

May 19, 2019

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.

OK

Apr 18, 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

篩選依據:

28376 - 机器学习 的 28400 個評論(共 28,998 個)

創建者 gursimran

Jan 16, 2018

the content given was good.

創建者 Christopher P

Feb 13, 2018

Good overview of machine learning as a subject. Introduces terminology and Andrew NG is very clear about what aspects of his lectures are necessary and what aspects are a deeper dive for enthusiasts.

創建者 Prasun T

Nov 07, 2019

Excellent Course! It would've been better if we used Python instead of Octave/Matlab.

創建者 IT

Nov 06, 2019

Cours intéressant

創建者 siyaram v

Oct 26, 2019

should focus on course content guidelines, there are error in formula in the course.

創建者 Keyrellous A

Oct 26, 2019

the course was amazing. Yet, I didn't like coding in OCTAV

創建者 Kim, S

Oct 26, 2019

You can start it easily. It requires you only basic knowledge of linear algebra, so it won't make you hesitate to study.

On the other hand, it would be much better if it offered more complicated examples for practice or more detailed explanations of models like motivation of using them or what is happening under it.

Still it was really great lecture since it offers very well designed exercises and good explanations.

創建者 Sean C

Oct 26, 2019

Mentors were excellent. Tutorials for programming assignments were excellent. Videos were very good. PDFs with the programming assignments were sometimes behind the times, too many unresolved errata.

創建者 Soumik B

Oct 26, 2019

Nice

創建者 Siarhei Y

Oct 31, 2019

Great introduction, but I am not sure that I can resolve real problems :)

創建者 Shane M

Oct 30, 2019

It's a great introduction, and covers many useful topics. Using MATLAB for ML makes code look more elegant if you understand MATLAB, but it's not very widely used outside of physics so using it for teaching Machine Learning is questionable. I also took particular issue with Andrew Ng's smug remarks about how anyone who takes this course is now "an expert in machine learning" or that 3 weeks in you already know more than many people doing ML in silicon valley. Statements like this are clearly false. I'm literally in silicon valley and there are no ML jobs to be had for anyone who doesn't have a dedicated degree and/or at least 3 years experience with whatever software they are using. And they never use MATLAB.

創建者 Rahul K

Nov 08, 2019

Amazing content. I think this is a must-learn course to start your journey towards the world of Machine Learning and Artificial Intelligence.

創建者 Kathleen R

Oct 28, 2019

Good course. I wish there was a bit more derivation/depth but it exceeded my expectations and I learned a lot.

創建者 Umair A

Oct 27, 2019

This course goes so much depth into the fundamentals of Machine Learning and its concepts. It is very useful to get deep understanding of the concepts. But, I will suggest making a few improvements in this course as it is too old and audio qualities are very low. and also, having some Python-based implementation will lead some industry-friendly applications

創建者 John H

Oct 27, 2019

Excellent curriculum. struggled with quiz wording; on many occasions, I simply did not understand the question as worded. Octave on OS X fiddly... The "pause" primitive in the MacPorts version of Octave didn't work and it was necessary to write an override;

The UX "course flow" of the Coursera material is inconsistent. For example, moving on from quizes requires a different navigation pattern than the rest of the course.

The exercise environment is good and the exercises are excellent; nonetheless, a large amount of preprocessing and "heavy infrastructure lifting" is done for the student. While I understand the practical considerations regarding avoiding distracting obstacles in order to focus on the core concepts; a collateral result is many students could vastly under appreciate how much preprocessing has been done on their behalf and take this for granted. Perhaps the student should be advised to understand how much preprocessing was done and how they need to understand the scope and effort involved; at least so they know to include budget and resources for this kind of Data Prep wok that ay be required;

創建者 Justin T

Oct 29, 2019

2nd round to get a certification for career development.

創建者 peter k

Oct 29, 2019

Very good introduction with some deeper dives where appropriate. The course notes are often hand written so taking your own notes in parallel is recommended.

創建者 Mohibullah S

Oct 29, 2019

While the course focuses on implementation of ML algorithms and their implementation, it would be very nice to have an explanation of the underlying mathematical concepts for these methods.

Also, it would be nice to have a big project, or a project that is done in stages as the weeks progress, towards a bigger real life application like the Photo OCR

創建者 Климов С В

Oct 27, 2019

It was one of the best courses I've had. The practical tasks were sometimes quite challenging yet quite narrow - you have to complete only a small part of the whole excercise I'd prefer to do the whole by myself.

創建者 Eric

Oct 27, 2019

This is put together well, but mostly covers techniques that have become less popular in favor of DNNs.

創建者 Hasibul I

Nov 11, 2019

Very Helpful to understand to see how the graph and calculation to get accurcy.

創建者 Achintya A

Nov 11, 2019

Very Good course. Helps you get in touch with the mathematics side as well as helps to develops a good intuitions for the algorithms.

創建者 asheem a

Nov 12, 2019

the best course for beginners

創建者 Vishnudev K

Nov 13, 2019

If they have used Python instead of octave I would have given a 5 stars.

But the theory is deep and solid. Even though the course is back from 2011, The content is still valid today.

創建者 Abdelrahman

Sep 13, 2019

Its very good course to start with, but the only disadvantage of this course is that it uses Matlab not Python