a. very good coverage of standard algorithmic approaches.

b. good suggestive guidelines on specifics of algorithms like issues / details one need to be careful, need not to bother etc..

c. broad coverage of examples..

d. tricky questions...good to experience...

Overall I liked this course content and the breadth of coverage. Based on the difficulty i experienced let me place some points of improvements that would help every student....

e. could have dealt some specific examples in full (from definition to implementation) as part of video lecture which would helped better understanding of the problems, algorithms, impact of specifics, implementation issues, analysis methods, inferences that could be derived, final expected solution.

f. expecting feedback on exercises.... not only correct or incorrect but reasoning for the responses could be of great help in better understanding....

g. downloadable videos could contain in video quiz...

h. Octave content could be increased.....

i. audio of the lectures needs fine tuning, hissing sounds could be filtered. For some of the lectures subtitles does not match at all...

Thank you very much for coursera....

Thank you very much Prof. Andrew Ng.....

Looking forward for mor courses related to ML by you....

The best Introductory Course on ML ever. No Pre-requisites allows anyone with the interest to learn ML learn it in the best possible manner. The course not only gives the Theory but also develops Intuition behind every algorithm which helps to retain the essence of the entire material. Not only the Theory but also the Practical Advices that the prof gives helps you to implement a ML Project from Scratch and Diagnose any possible error that may creep in, some of which aren't even used by many Industry Professionals. The prof is very humble and teaches you more like a friend, giving examples on how simple things may go wrong, also accepting that some of the concepts are not so easy to digest, so don't worry. The course is superbly organised which helps learners learn everything that the instructor wants to teach. The Quizzes, in-Lecture questions, Programming Exercises enable you to step through a path of- learning the theory, building the intuition, getting practical advices, implementing the code and inspiring you to work on your own projects. The Discussion Forum is always very active, you could clear any and every doubt of yours. Thanks and Congrats Prof. Andrew Ng on making the best MOOC ever!!

Excellent course. You will learn linear and logistic regression, SVM, neural networks and many more algorithms (supervised and unsupervised) You will also learn about how to evaluate the algorithms and how to design a more efficient system.

The teacher knows well how to teach the concepts in a way that you will gain a deep understanding rather than just memorizing formulas.

The quizzes are brilliantly designed to make sure you have learned the material entirely.

The computer assignments are created with profound details and you will do them in Octave or Matlab. Implementing the algorithms will help you fully understand what is happening in these algorithms. Enormous work has been done creating these assignments. Hats off to the designer of them.

The only thing that I would say could be better about this course, is the SVM topic. Unlike the other algorithms described in this course, the SVM algorithm has been explained in less detail than I expected (even with watching the optional videos). What I mean is, in order to gain a deep understanding of the SVM, one would need to see other sources as well. However, for any practical purposes, this algorithm has been explained well enough in this course.

Professor Ng definitely is gifted in the sense of turning complicated concepts into reachable, comprehendible ideas, which is the best thing I can expect when entering this complicated ML world.

To me, this class is more application-oriented rather than theory/math solid. Surely there are pros and the cons of doing that. One obvious advantage would be, even with limited math (mainly linear algebra and calculus) knowledge, ones can still have a great time playing with the ideas and models in ML field. The course definitely serves as a terrific introduction to this field. Andrew had all the ideas well-covered and made sure that after this class, students can apply these to real-world without bumping into some big troubles.

I don't think the lack of math proof or formula deduction can be fairly stated as the cons of this class. Though ones will definitely need to familiar those if they have serious ML jobs to be done, this class still serves as a great starting point to help people navigate which ideas/theories to dig deeper into.

To conclude, I highly recommend this class and encourage people who are looking for building more solid math foundations can find extra readings along the course :)

Excellent course. It has an easily understandable introduction and keeps gaining speed and complexity as you go on. While the first quizzes and assignments are quite easy, the later ones (except the final chapter) become a real challenge. I had to repeat some of them a few times. The additional resources are absolutely helpful, it's a shame I didn't use them until week 6 or 7.

I think by the end of the course everyone should understand what machine learning can do and what not, beautifully supported by real life examples. Until week 10 this course seriously left me wondering how machine learning is applied at all in a real-life work environment, but chapter 11 cleared things up.

The final chapter, as a whole, is the one where you know you've done the hard part. It's basically one big example to illustrate how you can apply your current knowledge on machine learning. It's your reward for all the patient learning.

You should still be aware that the world of machine learning is huge, and by learning the theory you merely scratch the surface of it if you complete this course. Me, for instance, I will move on to other courses to deepen my newly acquired skills, or to get some practical experience.

Exceptional. Andrew Ng brought a lot of himself in this class. He is a master of teaching complex topics in simple ways. I have learnt a lot from his teaching skill, in particularly on how to transform complex concepts into simple statements which is quite relevant in my job today. Not everyone is an engineer and yet many people around us have heard about ML. But many misconceptions are said. This class will help you make your message crystal clear. A big community has been growing up all those years and he deserves it.

I have started taking the class many years ago for free but had not the time to finish it because of a busy life as many of elearners here. Now it is done and I have paid for it without any regret. As a ML engineer, I had especially an eye into his ability to communicate complex concepts in simple way to his students. If you are a quantitative engineer, you may pass all 100% quite easily. But what matters here is not the hardness of the questions, it is all about listening to a talented teacher. I must say he masters the communication. He is an exceptional professor, a reference to teach online courses. I have taken many MOOCS for the past 5 years. This is easily a A++.

Wow! What I can say! Thoroughly enjoyed a computer science subject with plenty of Mathematics. And, that is at the age of 51. I enjoyed going through each of the Video and the subsequent notes and quizzes. The quizzes took lot of time and needed reviewing the materials again, for most quizzes. I initially struggled with programming exercises. The speed and my familiarity with matrix multiplication increased exponentially and finally finished the last few programming exercises in no time.

For an average Joe, I will recommend this course to take at a leisurely pace, referring to several materials outside. I too was reading couple of books while going through this course. The books really emphasized the learning. A couple of books most suitable to read along with this course are, Machine Learning by Tom Mitchell and Introduction to Machine Learning by Ethem Alpaydin.

This course improved my confidence tremendously as I was not programming hands-on for the past several years. I have not even used any IDE as most my programming experience was on Unix machines using vi editor. This course made a swift change in my thinking and imbibed confidence that I can code complex systems.

An amazing course! The lecture videos and slides are well-prepared and the concepts are explained by prof. Andrew in a clear and concise way, using neat graphs and plots when necessary. A lot of effort has gone into making the course largely self-contained. There's more focus on the application and practical implementations of machine learning algorithms than their mathematical and theoretical details (although not at all necessary, a fair exposure to advanced Linear Algebra - derivatives of multivariate functions, matrix decomposition, projections, etc. - can help in understanding some of the algorithms better). Lots of tips and tricks are given to help troubleshoot problems that often occur in practice.

The programming exercises are designed so that the student can focus on understanding the essential topics instead of getting bogged down in too many details (nevertheless, it's a good idea to briefly go through the functions and files already written by the staff). The quizzes are also well-designed and help the student recognize important nuances in the subjects.

There's a lot to be learned by taking this course! Thank you, Professor Andrew Ng and Coursera staff!

A really excellent course.

This is the first online course I've taken, so I cannot compare it to others, either on Coursera or other MOOC platforms, but I can say that it was perfect for my needs and I learnt a lot from it. The math content - of which, obviously, there must be a lot of - is very well-explained, and Andrew takes care to require little more than a high-school level expertise to understand.

The programming exercises were slightly challenging, but not overly so, and helped solidify understanding - and hey, there's always that little thrill of excitement when you see your program begin to give you real answers, and you realise that you've just written a program to recognise letters when given just a bunch of pixels.

I didn't use the forums much during the course myself, but they appeared to be very well-supported by knowledgeable and helpful mentors. (Tom Mosher, in particular, seemed to be on-hand all day, every day!)

As to Andrew Ng, the lecturer, he clearly has a deep and extensive knowledge of his subject matter, but his presentation is always kind, enthusiastic and helpful, so I'd like to pass on my thanks to him for making and presenting this course.

For the past few years, all I've been hearing is the word "Machine Learning" being thrown around. In my head, it was built up to be something really difficult that I had no idea about. I wanted to change that, I wanted to be a part of the conversation. This course has truly helped me do that, even as I go through more professional forums about machine learning, I understand the concepts a lot better. It's no longer technical jargon out of my reach.

Andrew Ng really breaks down the course to simpler elements. He brings up layer on layer of abstraction while keeping the students interested with real world application. The presentation, documentation and course assignments are planned perfectly. It's so simple to follow everything, and yet, you still gain more understanding as you move forward.

The community and discussion forums are a great help as well. I'd recommend this course to everyone looking forward to know more about Machine Learning! Don't let the math scare you, it's for your understanding, it's okay if you don't completely follow it. I'd suggest going through an intermediate maths course to relate to it better, but it's okay even if you go without.

An excellent course. Very well structured and well paced. The quizzes and problems in every week have been extremely well thought of and provide a very good insight into the concepts explained in that week. The barrier of 80% for clearing each quiz and each week's problems is very good and important.

Andrew NG is a very likable person and obviously comes with fantastic experience in the area of AI/DL/ML. There is one suggestion though. It is important for everyone taking this course to have a good understanding of linear algebra. So while Andrew does explain the mathematical concepts of each of the algorithms quite well, I believe he should not underplay the need for understanding that math even though some concepts are advanced. It is certainly important that everyone who takes the course, realizes that it is not just using an algorithm, but that the mathematical foundations underpinning the algorithm are equally important.

All in all I thoroughly enjoyed the course and will be taking up the Deep Learning and AI courses eventually, which Andrew has already developed.

Hats off to Andrew and team for a wonderful learning experience.

I am only 4 weeks in this course now. I am loving it!!

I must say, this course if very informative. I like the content, which is very precise yet easy to grasp. The course gives enough fundamentals, yet leave some of the finishing work, which is necessary to solve a particular problem, to be done by the students. For example I enjoyed thoroughly determining vectorized representation of the algorithms. Coming from High Level programming languages (I am a .NET developer), I had to unlearn easy way of implementing (For loops) and learn the new (and fun!) way of vectorized solution of Cost Function, Gradient Decent, Logistic Regression etc. Also I had to brush up some knowledge on calculus and matrix algebra from college days. Those are necessary to truly understand the beauty of these algorithm and working out an elegant vectorized solution.

Last but not the least, this is my 3rd Coursera course. This course provides me familiar experience, ease of using the platform, with all the great new knowledge in a concise format. I would like to express my gratitude to the trainer for a great learning experience and such an outstanding course.

This course is perfect if you are a beginner in Machine Learning and would like to get some gentle yet thorough exposure to the field.

Professor Ng is an enthusiastic teacher who presents the material in a very accessible fashion. He doesn't get too deep into mathematics but teaches you enough to get a sense for what exactly a learning algorithm is doing under the hood.

Some minor criticisms: The programming exercises each require you to complete some predefined functions with a couple of lines of code which, given the extensive instructions, is often trivial - here I would have wished for a steeper learning curve. Furthermore, I would have liked to hear about additional topics such as Decision Trees, Ensemble Learning and perhaps more about the different types of neural networks.

Nevertheless, I warmly recommend this course to anyone interested in Machine Learning. You'll walk away with a deep understanding of several key algorithms, some experience in how to implement them, some knowledge about real-world ML applications as well as a number of very useful guidelines for data preparation, model selection and error analysis.

Awesome class. I took it while also taking Data science and Machine learning at my school. I felt like it was very informative and actually explained a-lot of material better than my school teachers. I like how Ng went above and beyond to not only explain what are the different types of machine learning algorithms available, but also tips and tricks on how to properly use them and also explain industry insight into these problems. The difficulty for me was not too hard, there are many hints sprinkled around some of the assignments, and I like how clear and easy Ng explains the material, and he makes the effort to explain things from the ground up and sets up reminders, which i think is very important. I recommend taking this class as a basis for machine learning, however more study is required to learn about more advance topics in machine learning such as Deep Learning algorithms: LSTM, Generative adversarial neural nets, convolutional neural nets, etc. Take a look at this course's syllabus for a list of topics that are covered and plan your courses towards the complete set of what you want to learn.

This is the best college course I have ever taken! I have a MA in Psychology with emphasis on Statistics and Research Methodology and ABD (All But Dissertation) for a doctoral degree, and this class was better than any class I have ever taken. The lecture videos were organized, always on subject and extremely well done. I used to nearly fall asleep in some of my graduate seminars, but had no such problems watching Andrew's lectures. I especially appreciated the karaoke-like presentation of the videos + transcription. I have always done better when having textbooks to go to and take notes. With this approach, I was able to better process the information presented to me. The programming assignments were challenging but not impossible, and the tutorials for each assignments always seemed to provide the necessary clues to find the solution. And on completing the class, I feel that I have gained a significant amount of knowledge of Machine Learning, which provides me a bridge into a new knowledge domain. I highly recommend this class to anyone wishing to learn the basics of Machine Learning.

Great class. Much better than most I have attended in person. Excellent instruction, excellent resources, excellent programming exercises, excellent support in the forums, especially by Tom Mosher. Video is a much better medium than live lectures because of the flexibility, shorter segments, ability to stop and study something before going on, and ability to repeat when necessary. Great practice in vectorization. Excellent introductions to the necessary elements of ancillary topics. Bought the certificate. We live in a golden age for learning. Getting this kind of instruction would not have been possible for someone in my situation 30 years ago. I am grateful and looking forward to whatever I learn next.

Took this course to develop skills to work on artificial intelligence and other projects. One previous project described in article at http://www.kdnuggets.com/news/2007/n09/7i.html

Very satisfied. I have not been able to stop talking about how good this class is since I began taking it, and will continue to recommend it as the first step for anyone serious about the topic.

This course was very nicely done. Dr Ng's videos and narrative were excellent. They were long enough to convey the material properly and short enough not to loose my attention. Assignments were very good as they left you just enough room to fail, learn and ultimately succeed. The quizzes were thought provoking. On the questions that stated "choose all that apply," I would suggest that some form of feedback be provided so that the test taker could know which ones were incorrectly selected/not selected. Perhaps partial credit would be good instead of 0/20 with one wrong selection. Feedback, perhaps an explanation, would be appropriate on all questions incorrectly answered.

I would also suggest a pdf document that showed how to do the various matrix operations in octave with an example or two. This would include basic and advanced operations. I know linear algebra, I just didn't know the syntax in octave and this cost me 3-5 hours over the whole course.

Now off to do some simple applications here at work like spam filter and anomaly detection to start. Thanks for an excellent course.

This was a very introductory course to Machine Learning, very well taught by a very experienced Prof. Andrew. I will recommend people to take this course to understand the working of various machine learning algorithms conceptually. Although, various proves like Back-propagation, PCA, etc. are not explained in this course, you will never feel like being not able to grasp any of the contents of the videos. I personally watched the videos at 1.25x and it just went as good as it would have been at 1.0x, except for saving the time and completing the course in 6 weeks rather than 11.

Videos are very well organised and the instructor elaborates every section with as ease as any other. In short, I have become a fan of Prof. Andrew.

The only short-coming of this course is that it doesn't have any section dedicated to Bayesian Learning, Knowledge Discovery and few of the other basic topics related to Machine Learning. I will, therefore, request Prof. Andrew and Coursera team to give sometime developing one of the courses containing all the modules that have not been covered within this one.

Un cours excellent qui traite les principaux aspects du Machine Learning avec une ligne directrice sur la gestion de l'erreur et les différentes techniques qui visent à réduire cette erreur. NG présente les problèmes de réduction de cette erreur avec la gestion du Gradient et les différentes options pour éviter les minima locaux. Ensuite on comprend mieux l'impact des paramètres de régularisation pour la régression logistique ainsi que les spécificités des architectures neuronales. Le cours nécessite un investissement certain en temps pour comprendre le contenu et préparer les exercices sous Matlab mais on apprend beaucoup dans ce cours même sur des sujets plus complexes comme les SVM et les Kernels. Ensuite pour ceux qui veulent mieux comprendre les traitements de l'image quelques exemples (ce n'est pas mon domaine). A la fin NB aborde le Big Data avec Hadoop et la parallélisation des traitements (initiation). Il ne manque que les approches autour des techniques d'Arbres (absence totale) et les réseaux bayésiens ou algorithmes génétiques. Mais c'est un très bon cours

Can't say I am in any way not satisfied with the course, but here are a few personal feelings taking this course: 1. It is basically very straightforward to understand, although some part prof Ng takes extra time to care for some details, which I suspect for some students with solid math/stats foundation will find redundant, but indeed help those who don't a lot. 2. The algorithms introduced in this course are basic but also powerful, and relatively straightforward to understand too. 3. The programming exercises are very carefully designed to help students with the algorithms, while leaving the details of other programming components, which are very very very important to keep students on speed. 4. This course may require a little bit of Object-Oriented Programming language knowledge, and a little bit of calculus and stats to make the studies more smoothly. Thank you so very much prof Ng to have this course shared and this might actually turn out to be one of the most influential series in introduction to machine learning. - By some random fresher in the university

This was a phenomenal dive into Machine Learning! I will admit, not having a strong mathematical background, I struggled throughout the course, feeling like I was bobbing up and down, just managing to keep my head above water regarding some of the linear algebra involved (although the option linear algebra review unit was extremely helpful and much appreciated). That having been said, Professor Ng did an excellent job of not only teaching popular Machine Learning algorithms, and how to implement the same in either MATLAB or Octave, but he provided a wealth of practice advice for debugging and fine-tuning those algorithms as well as when and how to use them in real-world applications. This was my first course in Machine Learning and I enjoyed it very much, in spite of my struggle with the math. (I actually feel motivated to take some remedial math classes, i.e. linear algebra, statistics, and calculus in order to better understand the math behind these fascinating algorithms and to gain more comfort with what they actually do). Great course, invaluable information!

Great course from an expert in machine learning. It felt like the right amount of math - not so much as to derive everything from scratch, but enough to understand how the underlying algorithms work - what cost is being minimized, how gradient descent is used, etc. The programming exercises were quite good as well...not super easy but not too hard. I was initially skeptical of the choice of matlab/octave (rather than say python) but in the end it made sense. There is a lot to be said for grappling directly with the vectors/matrices and seeing things like how the weights are applied, how the sums can be vectorized, and similar "closer to the metal" aspects. Another terrific aspect to this course is that there is a fair amount of material on how best to apply machine learning, in terms of training, cross validation, test sets, understanding bias vs variance, learning curves, and understanding in general where to focus efforts next in a machine learning problem rather than spending months on something that would give minimal gain. In summary, well worth the effort.

This is an excellent survey course in Machine Learning for anyone who isn't an expert already. It moves at just the right pace to keep you challenged without being overwhelmed. The staff are very helpful, and the professor makes sure to get his point across before moving on. In fact, if I had to offer only one criticism it's that sometimes he will repeat the same thing over (many many times), which is unnecessary and thus sometimes frustrating because we have seek bars and speed control for the lectures.

It's quite remarkable how well this course communicates a high-level understanding of the concepts without bogging it down with much of the scary math that is often associated with ML. For those of us who are interested in getting into the nuts and bolts, the professor makes sure to name concepts so that they can be further researched at one's leisure. He gives you what you need to solve the problem, but doesn't do it for you.

I highly recommend this course for anybody interested in learning how many of the most useful technologies of this century actually work.

A really good course for an in-depth overview (is that an oxymoron?) of machine learning.

1 Prof Ng's teaching style is very good. The slides, his narration and his on-screen notes all combine together quite well to create a good learning experience.

2 The pre-requisites are not very heavy. If you've programmed in any language (not necessarily Octave) and you're willing to spend an hour revising some high school maths, you're good to go.

3 The course covers a representative set of techniques - linear and logistic regression, clustering, SVMs and basic neural networks.

4 The depth is not enough for you to become an expert in real-world application in any of these techniques. In my view, that would take a few weeks and a proper project in each of these techniques, which is beyond the scope of such a broad course. However, Prof Ng does go much deeper than just explaining the techniques. For each technique, there is good coverage of how to judge the end results and what to vary to tweak the efficacy of the technique. To me, this made it the perfect first course in ML.

Extremely well done course! Every video carefully explains the part of the concept being introduced. Whether its the derivation of a concrete formula, such as gradient descent, or a qualitative concept, such as the vector support machines, the tutor's explanations are always very clear and concise. I like that a lot of different ideas are covered, and even though I have a mathematical background, this course doesnt require it, since the most mathematical parts are left to the interested reader while the focus lies on the applications. A very beginner friendly course, all you need is some basic calculus and probability theory. Also , if its too easy for you: the notes of the actual Stanford University Course (!!) can be found in the materials section of the course, so you can "play the course on hard mode", too. That Stanford version is a lot more mathematical and difficult. All in all a very very good course, and I'm happy I tried it. I would probably do every course done by this tutor, he is that good of a lecturer. Coming from a maths and stats lecturer, btw.