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Learner Reviews & Feedback for Machine Learning by Stanford University

4.9
stars
169,785 ratings
43,417 reviews

About the Course

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....

Top reviews

RP

May 18, 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.

NN

Oct 14, 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

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101 - 125 of 10,000 Reviews for Machine Learning

By Andy M

Sep 8, 2018

Huge amounts of assumed understanding make this course impenetrable.

By rb3.1415

Jan 31, 2021

Broke my hype and fantasies about ML and created a sophisticated interest and a new perspective on the real challenges. Very good teaching and excellent online learning platform (shoutout to coursera)

By Caleo M S

May 23, 2020

Um curso incrível com uma ótima didática e exercícios que realmente estimulam o que foi aprendido em aula. Sem dúvida é a melhor fonte de conhecimento para adentrar no mundo de Máquina de Aprendizado.

By abbas k

May 30, 2019

so useful

By Alexey M

Apr 10, 2020

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!

By Alexander S

Jul 17, 2018

I think the rating depends on the expectations.

For a beginner with no prior background with Linear Algebra, statistics, Matlab etc, this is a good overview course (4 stars).

For a professional with prior background, I think this is a poor course (2 stars), because it fails to meet the expectation of learning a deeper understanding of the subject.

The materials covered were with low academic quality (suited for beginner students), any derivations or proofs are omitted if they are non-trivial. The theoretical background created is shallow. I didn't really get a good understanding of the fundamental tools and algorithms used in practical ML solutions, except for the simplest ones.

Yes, the course did give me some better background in ML. But the statement of Mr Ng that the course graduates are now ML experts is highly questionable.

In addition, there is a significant errata for part of the videos & slides - it would be nice to correct them. The fact that about a half of the course material has only slides (which are not always self-explanatory) and not structured course notes is also a point that I recommend improving.

By Jerome P

Mar 30, 2018

Good introduction course, giving an overview of machine learning algorithms and some methodology. Off course a lot can be added, but it's a good start for people with little to no knowledge or experience in this field. A few points that could be improved: I would like to have better material support for each section. Marked-up slides are not a great support for reviewing the different sections afterwards.

It would not hurt to provide a little bit more theoretical background and justification when covering the different algorithms. Andrew Ng almost apologizes when going into mathematical equations, but this is fundamental to machine learning.

quiz assignments are rather easy. They could be a little more challenging

I would rather have the programming assignment using R or python than Matlab.

But still a decent course overall I think.

By Aman J

Oct 6, 2020

I don't know why people have overated this course. I have attended other courses and they never skip the topics and jump to other. 1) The voice of Mr Andrew is horrible, its extremely low, and not consistent at all which is really very annoying, we have to look at the subtitles and rewind back and see actually what we explained on the screen. 2) The way he explains is really not good, I really have to re-run the lectures again and again to understand, as he jumps and don't explain why this/that happened. Everytime we have to search the forum for answers. Really not happy with the course.

By 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

.

By Andrea A

Oct 27, 2019

you have to teach this course with Python otherwise Octave is purely a waste of time. You need to keep up with time. Nobody in the financial industry uses Octave. Also, you need to show way more examples and exercises to allow students to absorb theoretical concepts.

By Igor U

Oct 8, 2020

The test questions don't match the lecture material. It seemed that the tests were written by another person. Also tests contain errors, because according to the lecture material, answer should be correct, but by marking it the system tell me that it's incorrect.

Having 12 years of experience in software development, I can say that the course was absolutely useless for me. I enrolled on the course because after registration on the home page of Coursera it appeared in popular block. And I didn't pay attention on negative reviews, henceforth this will not happen with me again.

By 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.

By Melinda N

Sep 4, 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!

By Arunesh G

Apr 20, 2020

The BEST course I ever had in my life, even better than a typical classroom based interactive teaching.

This course has the best mix of perfect pace and accurate (to the point) material.

With ample examples, accurate content, greater student-teacher interactions (via programming assignments, quizzes, etc...), and THE BEST TEACHER "Professor Andrew NG", this course is exceptionally the best course one can get in his/her life.

This course is best for beginners as well as intermediate learners.

In the video lectures, not even a sigle second is wasted on off-topic discussion. Each and every second is utilized to the fullest.

In this course, most derivations (complex ones) are skipped, but that is done to help us to focus on the core of machine learning rather than diverging somewhere else. Also, in the end Professor NG teaches about the ceiling analysis which is how and where to focus resources in the development of machine Learning Algorithm, which is not taught in most of the courses I have seen so far.

Overall, this is the best course one can get.

Thanks to Professor Andrew NG

By Muhammad S A

Apr 22, 2021

I am an experienced ML engineer and I have previously taken many different machine learning courses covering various sub-topics in detail and worked on multiple ML projects. This one covers the base theory the best. In practical terms, a lot of companies won't use MATLAB and I personally like Python more. That language issue is about the only shortcoming but I understand that it would be better for a beginner to use MATLAB instead.

By Emmanuel N

Dec 6, 2018

Amazing course. I had no idea of programming and my maths were more than rusted, but the way the lessons are taught, made the way a whole lot easier. If you're like me (zero programing and maths), it's no easy task to complete the course. But if you put the right amount of effort, patience and dedication, combined with the great videos and reference material, is totally doable.

By Nicholas D

May 14, 2019

Truly an exceptional class. Not often will someone with a deep proficiency in a discipline have the time or incentive to share their insights and teach to others; this class is a rare exception, and given the vital importance of machine learning to the future, I have a great appreciation and debt to Andrew Ng.

By Simin L

May 14, 2019

Great class! Should be recommended for every individual who wants to learn machine learning and don't have time or oppotunity to take a class at their own univerisity, this class is a guidance for the basis of machine learning and gives me instructions where to go next. Thank Ng really much.

By Yash B

May 25, 2019

This course was very well taught. There was a impressive focus on the basics and fundamentals of each topic. The lecture slides encapsulates the topics well and thus there was no such need of making my own notes which speeded up the learning process ;).

By Daniel

Dec 7, 2020

I used the python versions of the programming assignments (in the form of jupyter notebooks). Can't recommend enough.

By claire.hou0701@gmail.com

May 18, 2019

sehr gut!

By Alexander C

Jul 16, 2020

This was a great course, and I highly recommend it! Andrew Ng made me feel like he's my machine learning pal. I can see why this course is so popular.

I docked it a star because the assignments could really use an update. The work flow for completing them includes consulting multiple documents of (sometimes contradictory) instructions as well as errata documents, tutorial posts, and discussion threads. It's too much and when your script isn't working it makes it difficult to know whether you made a mistake or if maybe there's some updated note that you missed. If all of the assignment notes were just consolidated into one document, then five stars for sure!

By Jerome T

Mar 6, 2019

I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.

By MAHESH Y

Apr 9, 2019

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

By Daman A

Mar 28, 2020

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.