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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
149,457 個評分
38,079 條評論

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

SS

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

VB

Oct 03, 2016

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

篩選依據:

226 - 机器学习 的 250 個評論(共 10,000 個)

創建者 Bhanuprasad T

Jan 01, 2019

Loved it. Easy and Excellent Course

創建者 Luu V L

Aug 06, 2020

best ML course in the world !!

創建者 Jaspinder S V

Aug 08, 2015

Awesome course for beginners.

創建者 Mulat Y C

Feb 14, 2020

Machine Learning

Data Science

創建者 Mewada A J

Aug 06, 2020

best experience of learning

創建者 梁驰

Feb 08, 2020

喜欢吴恩达教授的课,讲的非常的好!教授很谦虚!赞赞赞!

創建者 chandan k

Jun 06, 2019

Great course to study!

創建者 Eugene M

Jan 04, 2019

Very useful course!

創建者 Joy F Y

Aug 07, 2015

It's very useful

創建者 Pavel K

Jun 06, 2019

A great course.

創建者 Hacker O

Jun 17, 2019

very good!!

創建者 Stephen M

Jun 05, 2019

Very useful

創建者 ylfgd

Jun 06, 2019

very good

創建者 Thierry L

Jan 04, 2019

Excellent

創建者 Saiful I A

Aug 07, 2015

Very Nice

創建者 Vivek K

Dec 13, 2018

Awsome

創建者 Lichen N

Aug 28, 2019

深入浅出

創建者 Sam C

Jan 02, 2020

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.

創建者 Saideep G

Apr 09, 2019

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.

創建者 MAHESH Y

Apr 09, 2019

it is one of the best course for beginners in machine learning, the only thing it lacks is its python implementation. If there is the python implementation of this course then no other course is better than this one

創建者 Doreen B

Jun 09, 2019

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.

創建者 Mohd F

Nov 08, 2018

There is a lot to say about you Andrew sir but in few words - "Thank you very much for teaching us the ML concepts in such a beautiful manner "

創建者 Mehdi E F

Mar 19, 2019

Very instructive course.

Thank you.

It would have been great to get an OCR exercice at the end.

創建者 Nils W

Mar 23, 2019

Great course, but the sound quality is quite bad.

創建者 Sai V P

Aug 05, 2019

Better upgrade from matlab to Python