Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.
Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.
By Arunesh G•
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•
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•
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•
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•
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•
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 Kevin H•
Programming exercises focus on the topics and provide you with good templates that you can easily fill in so you don't waste your time. Videos are very well done and quizzes are reasonable difficulty.
By Altanai B•
A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.
By Yashwanth N•
Amazing really felt that I learnt something substantial. Very happy that I chose this course over others Andrew Ng Sir explained everything very clearly to a required level of depth.
Thank you Sir!
I used the python versions of the programming assignments (in the form of jupyter notebooks). Can't recommend enough.
By Alexander C•
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•
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•
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 Alexey M•
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 Jerome P•
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•
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 Daman A•
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.
By Shitai Z•
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.
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 Ivan Č•
Certificate is expensive!
By Tibet M•
I was quite disappointed in this class where the exercises are too onerous and out of date. For example, Convolutional Neural Networks are not covered. Also, a lot of the material is dated from 6 years ago. There was also no help when I wanted to ask a question. When I asked where a certain material will be covered I did not get any response either. The last 1-2 sections were also wrong as I know that is not what is done in the industry. You will be disappointed if you take this course after a lot of work.
By Marcelo O•
i got stuck in one quiz i thought that maybe it was just me, i tried it a second time and got it wrong again.
I tried this quizz like 7 times and all of them were wrong so i took a photo with the sniping tool to check my good answers and then i tried inserting them but i just failed so this course or the program for grading doesnt work
By Germain M P•
Poor audio and video quality, what compromises the learning process
By Harry E•
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!