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
stars
124,695 個評分
30,575 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

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.

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:

176 - 机器学习 的 200 個評論(共 10,000 個)

創建者 Vyacheslav G

Feb 23, 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

創建者 Samuel

Feb 19, 2018

The course is not for people with not mathematical backgrounds plus its using matlab.. these days R and Python are more used in the industry for ML. I found to this course via friends that said it's hard but very recommended.. i think there are easier courses online that can deliver the same concepts

.

創建者 Anton

May 11, 2018

Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.

創建者 Loftur e

Sep 17, 2018

Assignments are very messy.

創建者 Ivan Č

Feb 24, 2016

Certificate is expensive!

創建者 Hu L

Feb 14, 2018

Too easy and too slow

創建者 Bayram K

Feb 17, 2017

I would rename this course as Programming Octave with Application to Machine Learning rather that Machine Learning. Once you start the course you will have to focus on Octave rather than on ML topics if you want to do programming exercises. There is no degree of freedom in programming. You are provided with a lot of weird Octave codes which you will have to complete instead of writing yourself from scratch. More than 50% of my time was spent in order to learn Octave and understand (guess!!!!) Octave codes.

So, if you really want to learn ML and try it in practice this course is not for you. However, you could just watch the videos whose level is not more that elementary introduction to ML.

創建者 Ross K

Oct 10, 2015

The course is more an exercise in flexing Ivy vernacular than it is actually teaching. The learning curve is too steep to be useful to the majority of potential registrants. You're interested in this course either to (a) learn something about an exciting and ever changing field and/or (b) to have the Stanford logo on your LinkedIn profile. In both cases, move on. The curve is far too steep to be useful or to merit the countless additional hours of background learning the course should have done to bridge the gap.

創建者 Larry C

Feb 24, 2016

There are too many mistakes and misleading statements made in the course material. There were a lot difficulties with submitting assignments in order to move forward in the course. I had to give up because I don't have time to be bogged down like this.

The students' comments and discussion would be useful if they can be accessed from within each lesson. I can't make heads or tails of what the discussions were referring to, when they are all clumped together at the course web site instead.

創建者 Alex W

Dec 14, 2015

The exercises lead you to the edge of a cliff, then push you off. No guidance. Good luck if you don't already know linear algebra, matrix math, and matlab. I'll be looking elsewhere to learn about Machine Learning. Glad I didn't pay for this course!

創建者 omri g

Nov 11, 2015

Been asked to re-take all assignments *after* paying for a certificate! I wil never pay for a Coursera course again, and I would not recommend my friends to do so

創建者 Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

創建者 Subham B

Aug 30, 2019

This course is definitely not for beginners.

創建者 Harry E

Oct 04, 2017

Before I go into why I liked this course so much, let me give a little context on my motivation to taking it. My background is a Bachelors in Math, and 9 years working in finance in a role involving very little computer science or statistics. I wanted a change of industries into the world of Data, for which a significant amount of learning and retraining were necessary; however before just enrolling on and committing to a masters degree, I wanted to answer some questions. Do I enjoy this? Am I able to learn it? Do I want to take this field a step further? Fortunately, the answer to all of my questions was positive.

I have to compare this to the ML module of JHU's Data Science specialisation, which I found rather frustrating as it was too brief to properly go into how the algorithms work. No discredit to the JHU team, I thought the overall course was great and served its purpose, but if you are like me and want to understand what's going on under the hood of these algorithms, this is a superb course. None of the maths is particularly hard, you will need to brush up on some linear algebra, and no prior Matlab is required. Some pretty tough concepts are built up from great simple motivating examples, for me the Neural Network / logic function was the best example of this, and I was extremely satisfied with how I grasped the material. There are enough real world applications thrown in to stay relevant (Data Science is a practical field after all), my favourite was seeing my predictions for number recognition appearing on the screen from the Neural Network I'd just trained appear on screen.

One critique I read of the course which I slightly sympathise with is that the programming assignments become a little like syntax exercises coding an equation into Octave, and thus lose their effectiveness in teaching you. I slightly agree with this and would love to have developed more parts of the algorithms myself, but with the limited time the course has, reading through the code of each of the exercises rather than just clicking through is a decent enough half way step. I would recommend everyone to do this, the point of the course is not just to pass the assignments, but to read around the material a little bit and follow exactly what's going on. That has to be left up to the student.

Overall, I feel like I'm equipped with what I need to get my hands dirty with some datasets to work on my own projects, and give Kaggle a crack. And that's pretty cool considering a few weeks ago I knew pretty much nothing about any of this. Onto the next step in my Data journey!

創建者 Anand R

Nov 20, 2017

To set some context: I am a graduate (PhD) in Computer Engineering from the University of Texas at Austin with over 10 years of experience in both academia and industry. My goal in taking this course was to learn the basics of Machine Learning, and understand what the current excitement about ML and AI is all about. I dedicated 3-4 hours every week, over the last 12 weeks, towards learning this course — and watched all the videos, reviewed all the lecture .pdfs and completed all the project assignments and all quizzes in the course on time.

About the course: This is one of the best courses I have taken (and I have taken more than 10 courses on coursera, edX and Udacity). Dr. Andrew Ng needs no certificate of approval from anyone. He is clearly a wonderful teacher, and I felt I struck a chord with him. There are few people who can explain complex concepts clearly without over-simplifying. Some people don’t have the ability, and often those who do, don’t care enough. The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down. Dr. Ng does that very well in several places and my deepest respects to him for doing that.

Clearly, Dr. Ng is a pactitioner in the field. The material was very well structured, very well paced and presented in bite-sized modules. The project assignments were both challenging and quite realistic. I feel a tremendous sense of confidence having completed this course, and I hope to try out some ML challenges on the web in the near future.

Last, but not the least, I cannot appreciate Dr. Ng more for the effort and dedication he has put into the subject and into his teaching. I felt a touch of nostalgia as the course ended suddenly with the last video (which was very moving, btw) and there was no NEXT button to click on. Being an educator myself, I know it takes a LOT of time and effort in developing a course. After completing this course, I felt I owed it to Dr. Ng. to purchase the course. I feel proud and happy to be certified as his student.

Thank you, Dr. Ng.

Thank you coursera.

創建者 Melinda N

Sep 04, 2015

Before starting this course, I had no previous knowledge of machine learning and I had never programmed in Octave and I have little/no programming skills. This is a 11-week course and so I was not sure if I would make it to the end (or even get through the first week) but I was keen to learn something new.

Positive Aspects: The course is extremely well structured, with short videos (and test questions to help us verify if we have understood the concepts), quizzes and assignments. Prof. Andrew Ng presents the concepts (some very difficult) in a clear and almost intuitive manner without going too much into detail with mathematical proofs, making the course accessible to anyone. The mentors were fantastic and provided prompt responses, links to tutorials and test cases, which all helped me get through the course.

Negative Aspects: Searching the Discussion Board for something specific was no easy task. I would have liked to have known the answers to some of the questions in the quizzes that I got wrong.

What I loved about this course: Learning how powerful vectorization is, it allows us to write several lines of code in one single line and can be much faster than using for-loops. I was wowed several times.

Prof. Andrew Ng is a great teacher. He is also extremely humble and very encouraging. During the course he often said, "It's ok if you don't understand this completely now. It also took me time to figure this out." This helped me a lot. He also said, "if you got through the assignments, you should consider yourself an expert!" and I laughed silly. By no means do I feel like an expert but now I have a basic understanding of the different types of learning algorithms, what they could be used for and more importantly this course has generated a spark in me to use this tool for things that I find interesting and for that I am very grateful. I don't think a teacher has ever thanked me for assisting a class. This is a first-time! So thank you Prof. Andrew Ng and everyone who worked to put this course together. Also, special thanks to Tom Mosher (mentor). My best MOOC so far!

創建者 Michael B

Dec 19, 2016

I would definitely recommend this course! I was very impressed by the quality of the lectures. Professor Ng uses the medium very well. He's easy to follow and the content is solid.The assignments were also good. They provide a ton of scaffolding, so you rarely have to write a lot of code, but if you never used Matlab before (like me) and it's been awhile since you've taken linear algebra (also true for me), then "thinking in terms of vectorization" takes a bit of getting used to. I'm really happy that I've been exposed to it, though, and it's pretty impressive how much computation you can express in one or two lines of Matlab.I only had to use the forums once at the beginning to figure out why I couldn't submit assignments. (It turned out that my version of Octave was too new for what the assignments had been tested with.) Once I got that sorted out, I never had to go back there for help, which I thought was a good sign that the assignments were clear and had been through sufficient testing by the staff.It's certainly a bit of a time commitment. I would probably budget at least 5 hours per week. I took a lot of notes, so I paused/rewound the videos a bunch, so it took longer for me to "watch" the videos than the advertised time.Again, the assignments were often not that much code, and I think they started to take me less time as I progressed through the course as I got more familiar with Octave and the style of the assignments. They aren't there to trick you or separate the wheat from the chaff: they're really there to reinforce the concepts from lecture and have you write some code yourself so you have some chance of writing your own code for your own project machine learning project one day.If anything, the assignments provide much more help than I expected. That is, if this were an in-person course where I could go to office hours or whatever if I got stuck, I would expect the assignments to provide less scaffolding and to force you to struggle quite a bit on your own more. (Maybe I just have bad flashbacks to undergrad or something.)

創建者 Malcolm N

Jan 11, 2016

My CS friend recommended me to take this course to learn more about how to use data in business, after he heard that I wanted to program an app for food. he warned me about the great deal of math involved (mainly linear algebra). me being a physics/engineering major I naturally got even more excited (it turned out that he was right, and it would also be a huge plus to know multivariate calculus, and I can see myself struggle with the concepts had I not studied both these topics to bits in school). incidentally, this was my first online coursera experience. I can tell you it will be life changing experience. No longer do I have to physically travel somewhere to listen to lectures or hand in assignments, nor download lecture notes off of the school server. This is a 24/7 always on always available service, with the best TA's to answer your questions if you get stuck on homework assignments and quizzes. Everything in the coding assignments tests your knowledge of the course lectures and is designed such that you can complete it in the shortest possible amount of time while reaping the maximum amount of benefit. It is "easy" sense does not require you to grind through mundane things like looking for your own training set data or writing code to plot and visualise the data, but it is "hard" in the sense that very often it takes an hour (or more) of studying the lectures and thinking to figure out how to solve the problem in the most efficient way as possible which often involves writing a single line of vectored matlab/octave code. It is more of an overview of the most important topics in machine learning, but will be a great springboard to go in depth into each aspect of it. Lastly, Andrew often offers wonderful insights into the day to day of machine learning professionals in his lecture videos, so I would advise watching every single minute of them to get the most out of the course instead of aiming to race over the finish line (which can be tempting at times when the deadline approaches)

創建者 Daniel J D

Jul 11, 2018

This course is vital. People can do machine learning using out-of-the-box tools like keras, fast.ai, theano, tensorflow, and do amazing things. But to understand what's going on internally, to understand what it takes to get things to converge fast and to perform accurately and to be as useful as possible, to understand various types of networks and new discoveries later on, it really takes a good, healthy, rigorous foundation at least in very simple calculus, matrix algebra, back-prop, stochastic gradient descent, linear and logical regression, and such. If you try to forge ahead and get stuck or cannot come up with a way to build a proper model later on, you may find yourself giving up or returning to the material provided in this course. Andrew Ng did an excellent job teaching this. Even so, I heartily recommend watching views from others to get unstuck or to reinforce what you have learned--to make it more concrete. And do all the assignments aiming for 100% on every one.I found myself viewing youtube videos from many experts and found most of them extremely interesting and exciting. By getting several people's perspective, I feel I was able to learn the material better and more easily. Of course, it helps to have a math background, too, and I received my BA in math long ago from Fresno State with an Applied Math option and a Physics minor. It was a joy to return to my old math stomping grounds.If it takes time to get through, that's OK. Sometimes it helps to let the material marinate or let your brain marinate in the material. Then if you're like me, you might come to the place where you start to get on a roll and decide you need to put everything else aside and focus on finishing *this* course to perfection. And it can open the door not only to interesting work but to other interesting and worthwhile certifications.

創建者 Pat L

Dec 01, 2019

This course is an essential tool. I am beginning on a long journey of machine learning I hope will end at my ultimate goal of securing employment in the field of natural language processing and deep reinforcement learning. Starting completely from scratch, I began this journey by getting text books on the topics and attempting to follow along. Many of the basic learning algorithms, which seemed so daunting at first, were explained to me in a way that allowed me to fully embrace and understand the topics on an intuitive level. This course is the essential entry point to anyone wanting to truly understand the mechanics of machine learning. The mathematical concepts are broken down in a way that is truly intuitive and easy to follow. Additionally, Andrew Ng is a world class instructor. His manner, presentation, and encouragement from, at the time of this review, 8 years ago is evergreen and invaluable. He sincerely believes anyone who puts the time into learning this material can accomplish great things in the world. This course was inspiring. I was so engrossed with the material that I completed all 11 weeks of course work within 5 weeks. My only issue is the use of MATLAB/Octave in this course. All the materials I have read state that these languages and applications are widely not used in the field anymore though at the time of the course development, I understand the inclusion. Perhaps an update to the course that allows for the programming to be done in Python or R would be beneficial, but once you get the hang of MATLAB the programming exercises become easier as the course moves on. My sincerest and most heartfelt recommendation goes to this course for anyone who has an interest in opening the door to their own journey into this field.

創建者 Alistair W

Jan 06, 2019

I worked for a start-up specialising in AI and rules based software that aims to learn how attorneys review contracts to extract key data points (e.g. clauses, parties, names, dates, numbers and text classification). Although I worked in sales / presales and as a domain expert for legal (being an attorney), I always wanted to know more about the technical side of what we were trying to achieve. This course provided my first proper and thorough introduction to machine learning, not only the coding concepts but also the underlying maths. It's been really tough going through the course, but that's down to my rusty maths (I have a maths A-level (i.e. pre-University maths to American readers)) and hadn't practised maths in around 12 years before completing this course. Similarly my coding skills had become rather rusty. All that said, the quality of teaching, forum support and coverage of this course has been great. I've had to read around quite a bit, mostly where certain topics were introduced without first zooming out to explain what the overall algorithm is trying to achieve. However, these issues were easily overcome given the course is well supported on the forums and similar topics are covered elsewhere on the internet given their prevalence today. I thoroughly recommend this course to anyone interested in AI and machine learning. I am looking forward to completing the Deep Learning specialization course as a result of this course, and will also be completing FastAi in tandem to get both a bottom up understanding (as was the case with this course, and will be the case with the deep learning specialization) and a top-down understanding (as will be the case with FastAI). Thanks to the team at Coursera, the forums and Andrew Ng!

創建者 Zdenek V

Aug 26, 2015

My review concerns partially the course and partially the Coursera concept as this was my first on-line course and I cannot distinguish between those two.My experience was simply great, I felt that it was time well spend, for example compares to my company provided trainings and I'm hoping to return to another course.I have quite strong background in statistics so some parts were too basic for me but being able to speed the videos up is neat and it opens the course to wide spectrum of students (again opposed to company provided trainings or even school lessons where in the best case half of the students are bored of the slow tempo and other half isn't able to keep up with the same tempo). To have my own timeplan also helps but I even finished quite early as it was so fun.About the course contend: Andrew Ng prepared great lessons, all was pretty well explained (even when he cannot use some "advanced math" not to lose part of the students). The examples were illustrative as well as realistic (at least seemed realistic and that makes it more exciting than some artificial ones).I was little afraid of the programming exercises as I've never worked with Matlab (using mainly R). But now I'm glad that I know another language:) The system of submitting works perfectly.I can say similar thinks about the contend of the exercises as I wrote about examples in lectures. It is nice to program my own spam filter - the concept of programming only some small parts of the program and not to have to deal with data loading, plotting... is again very entertaining and instructive and then it can be done some nice application in short time.My thanks to the whole Coursera team and of course Andrew Ng. Hope to use this cool tools in my work.

Zdeněk

創建者 Felix E

Oct 02, 2017

Absolutely fantastic course! I just wish my old university had classes on that level.

Fantastic tutor as well. I loved how Andrew would sometimes explain the same thing in different ways and spend time to start explaining the problem before offering a solution. I feel like this has been a great approach, especially since you can't directly ask questions in an online course and it would have been really hard to figure out the details on your own if some things are not clear to you.

Best part of the videos are the times that Andrew messed up something and probably thought "ah I'll cut this part out later" but never did. So sometimes you'll hear him stop in the middle of the sentence and just start all over. Fucking hilarious.

My only criticism (or rather, room for improvement): The programming exercises were kind of bland at some point since they didn't really require you to think about what you are doing. Most of the time it felt like I would just try to figure out the Octave language while basically transcribing the formulas in the PDF's into Octave syntax. Maybe offering another language (eg. Python+Numpy) as alternative would help here: Since developers will be more familiar/comfortable with a language such as Python, they can focus more on the actual ML implementation instead of spending about 75% of the time trying to figure out the syntax. Seriously, fuck the Octave syntax. And the IDE is atrocious.

But overall, fantastic course, 10/10, would recommend to everyone interested in ML. This course has basically single-handedly gotten me a job as ML developer since the first four weeks were enough for me to absolutely nail the technical job interview. Thanks Andrew, if I'll ever meet you, I'll buy you a beer!

創建者 Ron M

Nov 06, 2017

This course is a great balance of practical implementation and theoretical underpinnings. Very thoughtfully taught. My only complaint is more of an issue with the Coursera platform. If you run into problems, that in a physical university setting you could review in person with a TA, you can get help via the forums, but for the programming assignments in particular it is a challenge to talk to your peers about your problem, not violate the honor code, and still get to a point where you get your issue resolved so you understand how to complete the assignment. For example, while vectorized solutions are not essential for this class, they are highly encouraged, and in at least one assignment non-vectorized implementations pay a significant performance penalty (hours of run time - possibly to find it did not work properly). Ideally, in a physical college you could review things in person with a TA in a manner that did not violate the honor code, but did also get you to a true level of understanding. The open, public forums are not a substitute for that level of help, and while the "mentors" are good, helpful folks, they are also volunteers with their own lives and also limited by the Coursera platform itself. So that is the ultimate weakness with the Coursera format I am not sure the best solution, and it did not overly penalize me, but I can see people (especially on the Neural Network programming assignment) giving up and not completing the course because they could not get the level of understanding that is needed. I would still recommend Coursera, but hope as implementations are iterated the issue is addressed so that more people can get the help they might need.