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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
121,038 個評分
29,721 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

DW

Feb 20, 2016

Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.

ML

Aug 19, 2017

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

篩選依據:

28376 - 机器学习 的 28400 個評論(共 28,832 個)

創建者 Guillermo R S

Aug 04, 2019

It could include Python exercises.

創建者 Nicolas Y

Aug 06, 2019

This course is a great introduction to machine learning concepts, however, it is beginning to show its age.

創建者 Surajit D

Aug 04, 2019

I enjoyed a lot while learning. It's quite tough to complete but fortunately I ended up solving all the problems. Thanks Andrew Sir to simplify all the stuffs.

創建者 saisrikar b

Aug 06, 2019

a much better explaination

創建者 Tomas O H

Aug 07, 2019

Almost perfect, if done in Python I would give it 5 stars.

創建者 Satish C P

Aug 13, 2019

Good as an introductory course on AI relevant for a wider audience.

創建者 SREE K G

Aug 13, 2019

Best prerequisite for beginners.

創建者 Yury P

Aug 09, 2019

It's a good course but for me personally, math explanations given weren't deep enough. Though at many times the math part was just right - enough to give intuition but not too overwhelming. But sometimes there was no even intuition insight provided, e.g. why multivariate gaussian distribution can capture data correlations automatically. It is the question if this kind of questions should be a part of the course or not, but for me, they should have been. Another remark is that all test and programming exercises are way too "overguided". I mean for programming exercises solutions are basically written (at least not in Matlab but in English) in files exN.pdf. Questions in quizzes are very much based on precise lecture materials. Numbers may differ but the ideas are exactly the same. I'd prefer that to answer I would have had to do some thinking and generalizing what I'd just heard and not just remembered stuff.

創建者 Santiago H M

Aug 09, 2019

I would apreciate more engaging programming exercises with less helper code and more participation on the students side.

創建者 Sankalp K

Aug 08, 2019

Absolutely awesome course for beginners.

創建者 Anirudh H M

Aug 10, 2019

Well curated course, with an intuitive approach for beginners.

創建者 John M

Aug 14, 2019

Great course. Loved it from beginning to end. Maybe cite sources for the math behind the derivations and proofs throughout this course or at least go into more math.

創建者 Devaraju A

Aug 14, 2019

G

創建者 Yuqi Z

Aug 15, 2019

the lecturer skipped too many theories and mathematical derivations. also the programming homework is essentially to just code the cost function and gradients, rather than learning how to structure a ML system.

創建者 Thanasi M

Aug 15, 2019

Excellent groundwork for understanding the principles of machine learning. Could do with some updates and some more guidance around the programming assessments and more feedback in the quiz assessments.

創建者 Ariel S

Aug 12, 2019

It is very good for learning machine learning fundamental. Clear and simple explanations are always better. The audio should be improved for the future courses.

創建者 Kelvin T D

Aug 12, 2019

This course is a gold mine to machine learning. The instructor is great i also loved the practical programming assignments. I recommend this course to anyone starting out on machine learning.

創建者 Зорин М М

Aug 15, 2019

Sometimes the lack of translation into my native language was critical. In the course I did not have enough practical exercises. But I feel that the course has benefited me, it was very interesting to test myself for my ability to bring difficult tasks to a victorious end. In general, the course was often pleasing, thanks to Professor Andrew Ng for his work!!!

創建者 Mutyala s c

May 21, 2019

the teaching is very well an there is no problem in unerstaning concepts but only problem is that i am not able to submit ASSIGNMENT due to some technical issues.can i complete this course without submitting assignments?

創建者 成文辉

May 08, 2019

Why lower the complexity comparing with https://see.stanford.edu/Course/CS229

創建者 Venkateswara P S

May 08, 2019

It is a great course. Symbols like Theta , Delta, MU might through you off initially. Once you start working on some examples and programming exercises, your confidence levels goes up. For me it took more time than the estimated time. You will certainly learn a lot!

創建者 Lysimachos E M

May 08, 2019

Great tutorials offering a really fast way of learning maching learning techniques and philosophy.

The the exercises in matlab is not well defined

創建者 Aviel L

May 08, 2019

It's a good course. It gives the basic foundations. As for the exercises - everything is pre-cooked and all you need to do is fill the missing code. It's too focused and too easy. The relevancy of the exercises is questionable because end of the day you are told EXACTLY what you need to do and your work is to typically code it, which means simple for loops, matrix multiplications, very basic. Far from what you will have to go through if you really had to work on a true project. Also the tests are super easy and are made such that you will pass the course and be happy. End of the day you don't really remember and haven't confronted and digested the methods and ideas. Andrew is really a very nice guy!

創建者 Thomas R

May 10, 2019

Very good teaching.

Exercice are a bit too simple and does not forces you to really think about ML issues, they are mainly translation from formula to octave/matlab.

創建者 Andrei G

May 23, 2019

Bunch of parameters in the practice problems, some better javadoc thingie would be super useful