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

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.

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 "

Very instructive course.

Thank you.

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

Great course, but the sound quality is quite bad.

Better upgrade from matlab to Python

Well, this course has at least 3 undeniable cons:

1. It exist;

2. It offers certificate for reasonable and affordable price;

3. It has "Stanford" in title.

Still, it could be improved in many ways.

First of all, it has poor video and audio quality, maybe worst I've personally seen in MOOC. Dear Stanford! Professor Ng is cool, give him room with windows, 1080p camera and microphone! Even less famous educational establishments can afford it.

Second, subtitles are also poor. English is not my native language but I dropped subs in my language after first try. English subtitles also have a lot of errors: many words are garbled with homonyms; I'm lucky to have some background in course theme and without it I would be completely lost trying to understand what's even going on.

Third, I think this and many other courses are suffering from past teaching system and experience. What is classical teaching system? There is lecturer narrating and writing on the board, sometimes showing something; there are students listening and taking notes. Well, still better than "watch your master working, nothing will be explained" method (still present in some cultures), but what century it is? XVII, XIX? We are learning "Machine Learning" via Internet, and watching materials being hand-written in process? Seriously? Even basic HTML skills in this days are enough to show formula, where you can get reminders of it's every part by simply moving cursor on it (Wikipedia is one example). After two weeks break in learning it will be very effective way to remember fast "what's going on, why this formula is so big and what the hell is that squiggle", and learning process will be improved greatly.

Little more HTML effort, and there will be way to live demonstrate curves, planes and how different parameters affect them; it will be possible to let students experiment while learning which is great improvement for learning, memorizing and understanding.

These are just examples, but hopefully my point is clear.

Quizes are too easy, solvable with "hey he just said that" method and some intuition, not require deep understanding.

Programming assignments are well prepared and explained, but programming materials amount is not enough for me.

Thank you professor Ng for your efforts!

This course needs to be severely updated and fixed. It is mostly kept alive by the amazing community of mentors, in particular, Tom Mosher. Without Tom, I would have gotten extremely frustrated with the weird quirks that come about during assignments. One important piece of advice: if you can do assignments in an Octave environment such as GNU Octave 4.0.3, I'd strongly recommend it (Althought it tends to crash ofter, so save, save, save!!!).

The course needs a platform where people can actually apply all techniques independently and learn by way of being graded on their accuracies in prediction. Otherwise the assignments just become a mere copy-paste mechanism of the formulae provided in the pdfs.

Too easy for people with background in machine learning. But would be a good introductory one if you have zero understanding in machine learning and want to change your career track.

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

Content was OK, but quality of teaching was fair at best -- important points glossed over, many not made clear at all, some simply omitted: Bayes classifiers, decision trees, etc, etc.. Audio visual quality of lectures poor. Ng's onscreen scrawls and voice recording were terrible, and there were many mistakes in graphics. Numerous typographical errors in exercise instruction .pdf's. Exercise text itself (ex__.m files) had numerous "pauses" that failed to instruct the user what he had to do (or not do) next, so you had to carefully examine what followed. If more care was put into exercise construction, the "pause" text in the command window would not just say "Enter to continue" but say what coding action was needed to continue. Obviously a lot of work has already been done on interactivity: Quizzes, online Submit scripts, which for me all worked extremely well. But clearly the course could use a lot of improvement in many aspects. Thus I grade it: C-

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

Assignments are very messy.

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

My feeling is that the author of this course has no idea what is "Machine learning" - I have the impression that he repeats slogans which he does not understand.

This course is definitely not for beginners.

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!

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!

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.)

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)

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.

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.

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!