Chevron Left
返回到 机器学习

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:

27776 - 机器学习 的 27800 個評論(共 28,998 個)

創建者 Vivek P

Aug 23, 2017

Very nice course to kick start with Machine Learning.

創建者 Pradeep S

Apr 04, 2017

Teaches you concepts of regression, basics to start of with Machine Learning. Assignments could be slightly modified and applied to many real life problems. If one can find a real time project, and apply the concepts they learn from this course they can make total use of it.

創建者 Naveen K

Sep 20, 2017

Wonderful insightful.

創建者 Aniruddha B

May 21, 2018

Good content, especially on supervised learning part.

創建者 Hauru

May 14, 2017

With acceptable technique using in data mining, Andrew did give us some intuition in ML. Although it need to further learning on some Bayesian rules and math, but it's really worths it.

創建者 Alex A A

Jan 23, 2018

Please make this course compatible with python as well

創建者 Hojjat S

Mar 15, 2017

Very good course on introduction to M.L.

創建者 Archit J

Jan 24, 2018

This course has been an amazing journey, learning with Andrew was really fun. Love and blessings, Andrew. Thank you:)

創建者 JACOB D

Sep 25, 2017

good

創建者 Carlos M d C T

Jun 01, 2017

First class that I took at Coursera and first introduction to this field of Machine Learning.

Nice narrator, good overview of several topics. Very nice problems to fix our understanding.

Only negative side: For a master graduate in science/engineering, it is maybe to informal. More mathematical formalism and explanations would maybe be more interesting.

創建者 Amit K

Jul 03, 2017

A perfect course to start in this vast field of machine learning starting with the roots of concepts using basic mathematical tools like matlab and octave

創建者 Andreas H

Aug 09, 2017

Great class, focusing a lot on "intuition" about the algorithms and teaching how to get a "feeling" for the -- often quite complex -- input structures, like matrices and vectors. The programming exercises make the problems plastic and understandable. As a physics major (who had no problems with the math) I enjoyed that the focus was completely on use cases and concrete examples and not on theoretical problems, like maybe convergence criteria, possible exceptions of rules, etc.

What I would criticise is that the course sometimes makes the impression that not all slides are perfectly up to date. The examples are often quite old. And while they still are perfectly delivering the message, I would prefer at least some connection to today's fast-moving developments in ML.

As someone who is new in the field it was great to see how you can get quick results in Octave/MATLAB, but some examples that use TensorFlow (with e.g. python) would have been really nice as well.

All in all an interesting and entertaining course from which I learned a lot!

創建者 Roberto M

Aug 27, 2017

This course is quit hard, specially during the Programming Assignments. Some quizzes were hard, but for the most part I will recommend this course to everyone that will like to learn Machine Learning.

創建者 PAVAN S

Aug 28, 2017

Very Informative. ML Concepts are explained in simple term. Highly recommended one for beginers in Machine learning .

創建者 Tapan M

Mar 17, 2018

H

e

l

p

e

r

C

O

d

e

c

s

h

o

u

l

d

n

o

t

b

e

g

i

v

e

n

.

創建者 Hamish O

Oct 05, 2017

Really enjoyed the class. A nicely balanced introduction to a topic I didn't know much about before. Prof Ng is very positive and engaging, and clearly has taken the time to think about how to explain the concepts in an accessible way. I would have liked more programming exercises for the last two weeks, and also some suggestions on what to do next in the final video. There are some resources to choose from hidden in the resources tab but I'm not quite sure what to do next. I guess that this is still a positive sign for the class - I'm itching for more!

創建者 Alex S

Aug 24, 2017

Great, just some of the assignments are a bit frustrating simply because you aren't given clear instructions on what you are supposed to do. Getting assistance with code is also near impossible for some questions. Overall I learned a lot and enjoyed the class, but there is still a bit of room for improvement I think.

創建者 Mukesh K

Dec 19, 2017

Great course and well explained algorithms

Best part : Explanation of Algorithms, real time ML scenarios.

Can be improved : Addition of programming in course. Assignment is too hard to complete.

創建者 Philip C F

Feb 19, 2018

Excellent overview of concepts. Practical in the sense that you learn everything to be highly knowledgable about all aspects of machine learning if you pay attention. The assignments, however, seem to spend way too much time learning vectorization aspects of octave and debugging matrix manipulations in that language. It would be better to either:

1) Focus on the concepts by writing more code in the assignments even though that code will be not truly practical (e.g., writing out the full routines in for loops so you have a sense of all that is going on under the hood to deepen understanding)

-or-

2) Modernize to python which basically everyone is using so you are truly practically able to do machine learning in a job setting

I know I'll be taking a python class next.

創建者 NAVEEN K M

Jun 12, 2018

This course is really helpful for beginners as this course teaches you the basics of almost every aspect of machine learning so if you really want to build intelligent machine learning algorithms you should really do this course

創建者 Luke K

Apr 29, 2018

Andrew Ng's lectures are informative and well-structured, and homeworks do a decent job of reinforcing the learned material.

However, I feel that too much of the code was pre-written. I have put in considerable time to remedy this, but I feel that someone who has just completed the homeworks may not be fully confident starting any such problem from scratch.

創建者 David S

Jan 14, 2018

A good introduction to the domain. Decidedly light on important mathematics, but a few hours on Wikipedia and some ML blogs back-fill that information for the greatest part. It could probably use an update to reflect the actual state-of-the-art in 2018.

創建者 Rishabh R

Feb 08, 2018

go for it if its your 1st course

創建者 Carlos R

Jan 27, 2018

Very nice course. Maybe it misses some more practical examples, but I've learned a lot :)

創建者 Fortunatus O

May 29, 2018

knowledge of Machine Learning algorithms does not only help you solve personal real life problems but it can help you solve national and even international problems