Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.
Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!
автор: Iain M•
Andrew Ng's passion for the subject of Machine Learning is obvious and infuses every lesson. His wide experience in the field allows him to enhance the video lectures with tips and examples that help him to explain what are often quite complex concepts.
The lectures are very well organized, clearly presented and, although they cover some very advanced techniques, are obviously aimed at those new to Machine Learning. The programming asignments include clear and detailed instructions. In fact, if I have one criticism of the course it is that those instructions may actually be a little too detailed - occasionally involving little more than copying and pasting code from the instructions into Octave rather than writing our own scripts.
I really enjoyed this course. If you are a beginner, then I think you'll find this is an excellent introduction to Machine Learning. If you have a little more background knowledge then this course will help you consolidate and build on that knowledge.
автор: Marco C•
A fantastic opportunity to get a global overview of one the most exciting topics of data science.
Lectures by Mr Andrew Ng are well structured, perfectly declined in both illustrating the need behind each development and in rigorously explaining its logic. He drives you through a step by step path and always helps in understanding the overall context with clear examples. Each video is stopped now and by a not graded quiz aimed to check you are perfectly in line with the concepts. Grades are obtained through the questionnaires (five questions each time, you need to get no less than four correct answers out of them) and a programming exercise in Octave/Matlab. Especially in order to well deal the programming exercises, the discussion forum and the kind availability of the course mentors are a great resource!
In conclusion a seriously challenging course, that will take lot of your time but it's definitely worth! Many thanks Coursera and really thanks and congratulations Mr Ng.
автор: Xinguo W•
Just completed the course myself and I have to say this is a great course for anyone who wants to get a comprehensive understanding of Machine Learning. First of all, the content of the course is very well structured. It covers a lot of machine learning algorithms and also includes a lot of practical applications. Professor Ng is very gifted in teaching and he can explain some difficult topics in very simple terms. I also found he is very engaging and the quick questions inserted in the middle of the videos are very helpful to keep the students focused on the lecture. The programming assignments are at the right level of difficulty, and I found the instruction for each assignment works like a great summary of the corresponding materials. Didn't use their discussion forum much, but for a couple times I used, the mentor was able to respond in a very timely manner. Overall, this is a great course and I am so happy to be able to take it myself. Thank you, Professor Ng!
автор: Nikhil G•
I came in with no background in linear algebra/octave/MatLab, and machine learning always seemed like this black box to me. This course had some challenging sections, but is totally doable for someone with a limited background, if you're willing to put in the work. The lectures simplify the concepts into little, manageable pieces, and the programming exercises reinforce the concepts learned from lectures (these exercises also have detailed tutorials to help those out that aren't familiar with the programming languages used in the course). Dr. Ng has an amazing ability to teach without jargon, and being overly technical. His final video, and mannerisms throughout the course, make it clear that he is a compassionate instructor that really wants to inspire his students to learn these concepts. Some of the ideas behind machine learning are now much more clear to me and I look forward to learning more, and using more current implementations of machine learning e.g. w/ python.
Very helpful course that taught me the basic principles behind the field of machine learning and its various applications in the world. Mister Andrew did a great job teaching, and his love for the topic made the whole experience even more exciting for the student. The videos were short and straight to the point with various questions and quizzes that constantly held the attention of the student and helped him keep his focus, while the programming assignments gave a very good intuition about the practical use of machine learning principles in real world problems and helped the student gain a first- hand knowledge about machine learning application programing. The tutorials were very useful and the mentors replied to my questions very fast, giving me the help I required while working on an assignment. I thank Andrew and the mentors for helping me embark on a journey towards the world of artificial intelligence, machine learning, robots and technology. A great course indeed.
автор: Zoltan K•
A practical and engineering minded introductory/overview course to machine learning. It has set the scope of the subjects right, it was wide and deep enough to be able to understand the basic ideas, how to attack the problems, the type of thinking needed for solving problems with machine learning, how to plan the work, where to spend more time/energy, how to implement efficiently, how to measure performance and progress etc. The choice of Octave for the programming assignments proved to be excellent. It was fast to grasp its concepts and very efficient both at writing the programs and running them. The videos are transcripted, the slides were well explained, they are available for download, the resources section contained the summary of the lessons etc. All in all there's been a lot of progress from the first Coursera courses many years ago. The Coursera app (Android) was surprisingly good and useful, I preferred using that for watching and I used a browser for the exams.
автор: Digvijay D•
I've been utilizing my free time to learn the concepts of machine learning and their applications and successfully completed the Machine Learning course offered by Stanford on Coursera.Professor Andrew Ng is a great teacher and this course is both challenging and satisfying. The course is of 11 weeks and some weeks have two sets of lectures. So there is a little more effort required in this course than any other ML courses but is great value for the time spent.This course gives a grand picture on how ML works by focusing more on the basic concepts as opposed to focusing on specific components like programming language/libraries which most of the ML courses available on the internet suffer from.What I loved the most about this course is how the instructor(Andrew Ng) always mentions the correct way of doing things and how things are done by a few people in industry. Overall this is a very good course which gives a solid foundation in the basic concepts of Machine Learning.
автор: Guilherme C d A S•
Fiz o curso em 2020. Sou estudante de Economia (já tinha uma base boa de estatística), tinha um conhecimento básico de programação e conhecia um pouco de Machine Learning. Na minha opinião, Andrew Ng tem uma boa didática e é muito bacana aprender com alguém de renome na área. O curso serviu para dar boas noções de ML, tornou o assunto muito mais tangível e deu um panorama interessante. As tarefas de programação me tomaram um bom tempo e tive algumas dificuldades mas consegui completar tudo com alguma ajuda. O programa utilizado é o Octave (ou Matlab). Não foi difícil me adaptar a esse ambiente. No entanto, me parece que a escolha de software pode ter ficado um pouco ultrapassada, talvez seria mais interessante em Python. Não sei dizer o quão difícil é adaptar o código e as bibliotecas. De qualquer forma, isso não afeta a explicação dos modelos de ML ou mesmo os conselhos e boas práticas apresentados. Não tenho base para comparar com outros cursos de ML disponíveis.
автор: Kevin S•
The positives of the course are: Material presented was clear, and concise, not a lot of fluff and thus very efficient. The pace was just right for absorbing the material and to write some notes. Besides the excellent delivery of the material, what really stands out about this course for me and why it is so awesome is that there was strong coverage of methods to use to avoid possible pitfalls (underfitting, overfitting, types of problems that each learning method is suited for, how to decide on spending extra effort gathering data or not, finding which component in a pipeline is worth trying to improve and avoiding wasting effort on components that don't improve overall results). Other courses will often present a range of different methods but have little or no guidance on how to use them correctly and avoid pitfalls. Anyone can use a tool but often it can make a big difference in efforts and results if it's used correctly.
The negatives of this course are: none :)
автор: Himanshu G•
Thank You Very Much, Andrew Sir. I feel the scarcity of words to express my gratitude and thank you to you, Sir and the Coursera. The course is very well designed and enables a novice like me who has little prior exposure to the field of computer science to understand the concepts of Machine Learning, pass the review exercises and programming exercises. I think a little improvement could be if more videos related to Mathematical concepts (for example something relevant to Support Vector Machine) relevant for this course are included in the course or authentic links or (esp. Mathematical) resources are provided which students can refer to sharpen their mathematical concepts which are relevant for this course. Thank You very Much, Coursera for providing me financial assistance. I guess without the financial assistance, it would have been hard for me to enrol to this course because I earn very modest from my current job. Thank You Very Much Coursera and Andrew Sir.
автор: Josh F•
Excellent course. I have no background in math (save for a good understanding of linear regression) but professor NG's teaching is so good I was able to follow along quite well. I knew I would eventually be working in python, so I personally elected to forego the assignments in matlab/octave and found a resource online that had all of the finished assignments in python which I simply studied and commented until I understood them. I would recommend this approach if you are simply interested in getting up an running quickly and know you will be using python. I would also recommend watching the videos at 2x speed to save time.
The only criticism that would be possible to levy would be that he did not go deep enough into the math in some areas but on the other hand, I may have been lost if he had. I was really appreciative that he was encouraging enough to say "it's ok if you don't fully understand the math, it will work regardless". Solid course, absolutely amazing,
автор: Kai C•
I LOVED THIS COURSE. SO. FRICKING. MUCH. Professor Ng is very thorough and obviously cares a lot about what he's teaching. I feel like I have a really solid foundation in the concepts of Machine Learning after taking this course, and I would highly recommend it to anyone else interested in it.
A word of advice to prospective students -- linear algebra is your friend, and 3Blue1Brown on YouTube's Essence of Linear Algebra playlist is of near infinite value as a companion to Professor Ng's instruction.
This is definitely a course where you get out what you put in. If you're willing to work through the quizzes and maybe take out a paper and pencil to diagram out some of the matrix math, as well as grind out the programming exercises, you can learn a LOT. Professor Ng is one of the leading experts on Machine Learning in the entire world, and the fact that I can learn about this field from him for FREE is absolutely incredible. So thankful to have taken this course.
автор: Tianhong Y•
Prof. Ng is such a good teacher that he explains things in a proper way to make you understand.
He has a profound understanding of the details and derivations behind the knowledge and conclusions. If you have a relative background, you would have a chance to think about the knowledge in a deep way. If not, you can still get the main idea and be able to use it, and you know what is lack and where to learn it.
Overall the lecture notes and videos, the quiz and assignments are all good, full of thought about how to make students follow the course and understand better, as well as exercising with real applications.
I didn't realize that the machine learning course was the first one on Coursera and Prof. Ng is the founder of this wonderful platform, until toward the end of the course. No wonder the quality of this course is so good. I learned a lot and would recommend it to anyone with a good math or physics background and want to learn Machine Learning seriously.
автор: Daniel A R•
As this course is rated, and according to the lots of opinions written about this course, I can only add a new congratulations remark to their creators. Andrew Ng is not only a genius who masters all the contents, he is also really didactic and teaching. Andrew is able to boil the more complex concepts (e.g.: neural networks) in simple explanations with very illustrative material and an updated approach to real examples and use cases like (autonomous drive or Photo OCR and text recognition).
I would like to thank you the great support provided by Tom Mosher in the Discussion Forum (this is one of best forums I've checked in the different MOOCs and the main reason is the fantastic work done by Tom who gives quick and intelligent answers focused on making you think and learn about the questions or doubts you ask).
I think this course is a must for all those who are into data engineering, data processing and especially Machine learning or Artificial intelligence.
автор: Bob H•
Excellent course, Professor Ng teaching approach works very well for complicated but fascinating subject. I always found his lectures to be clear and concise regardless of the difficulty of the material. I also found the programming assignments to be a valuable tool to enhance understanding of the material.
At the conclusion of the course I feel I have an excellent grasp of the topics that were presented in this course. I have found additional materials on the internet (e.g. course syllabus from CS 229 that Professor Ng teaches) such as papers and books covering aspects of Machine Learning. I am now equipped to continue my learning using more advanced material. I am now rereading the Master Algorithm by Professor Domingos and I find that I now have a improved comprehension of the material presented in the book.
The only thing I could wish for is additional material and assignments for other learning approaches, e.g. Markov Chains, Naïve Bayes etc.
автор: Sonya L•
This course has good learning material, home assignment, companion material as well as resources. I consider it as a temple that I have to visit and to step over to lead to a bountiful machine learning world. The biggest complaint I have is that some course video and slides have a lot of errors, especially neural network part. Yes, there are errata to correct them. However, it takes time to cross check. That might misleads students before they detect them. Also some of intuitive lecture might not be concise enough and might mislead students too. Having say that it definitively worthwhile to take this course for people who haven't had formal machine learning academic background. It's very tough to juggle busy work, family and perseverate on 3-month course (not to say finish challenging home assignment) . It gave me a lot of stress. I am glad that I push through it. At the end, it is worthwhile. Thanks to Professor Ng and TAs' great works.
автор: int s•
I learned a lot from this course. I recommend any beginner (like me) or a professional in this field may try this course, because
1. I have learned types of mathematical learning
2. I have learned how to prepare myself to proceed step by step to solve an ML problem in future, instead of just jump into the problem and try to solve
3. Not less not heavy but Andrew has shown me the actual mathetics behind the algorithms.
4. I have learned to find a bug in a model and how to approach it to debug the same. Those parts are the best parts of this course I have enjoyed.
6. I learned how to decide the hypothesis, how decide the polynomials, how to decide parameters, how to decide threshold value (instead of guessing[Classification Problems]), how to choose and/or synthesis features and many more.
5. The last thing I should mention, Andrew taught me how to evaluate an algorithm with a simple number(real number) whether it is working fine or not.
Thank you Mr. Andrew Ng
автор: Jason J D•
This is probably the best Machine Learning course out there. The course covers up everything in Machine Learning, right from the basics to the complex parts. Even though I had studied some Machine Learning at college, this course helped me learn many new concepts that I was previously unaware of. The instructor Prof. Andrew Ng is very good. His explanations and examples are simple, yet cover up all the details. The course structure is very good and the assignments are well prepared. The course also gives a tutorial on Octave / Matlab basics and helps develop your logic and coding skills in the same, through programming assignments. The course material like the Lecture Slides are very useful as well. This course not only helps you learn Machine Learning, but it also helps you develop the intricate details used to implement Machine Learning in daily as well as industrial applications. I would recommend this course to anyone interested in Machine Learning.
автор: Keiji H•
You can learn everything about machine learning from the very basic things to the now omnipresent product recommenders and spam removers such as Amazon's and Gmail's. The course consists of lots of short, 0-15 minutes, lecture videos and programming assignments, so you can see them at your intermediate times though you will need a certain amount of time to complete each assignment, which would greatly help you understand how they work and make you feel like you could make your own algorithms yourself. Don’t worry about the programming environment. You can see how to install it on your computer, either Mac or PC, in the course. In my case, I’ve completed all using Online Octave, in which you can run your program without installing anything on your machine because it runs online though the computing power you can use is limited. Anyway, I truly appreciate Andrew Ng, the creator of this course and the co-founder of Coursera, to give this great opportunity.
автор: Tan M•
It has been a great learning experience taking this Course. I am currently taking an advance version of Machine Learning in my school, and this course on Cousera has definitely provided me the basic and essential knowledge in tackling more advance machine learning problems in school.
To the mentors, thank you for answering my questions that i have posted in the forum. Just a little feedback, i hope that there will be different mentors tackling on different weeks' problems (Spreading the workload ..maybe). In this way, answers to some of the questions can be more detailed.
And i also hope that some days the errata in videos can be corrected even though these are minor errors...so that students do not need to refer so much to the errata page while watching the video lectures.
Overall, the course is great and i will definitely recommend this to my friends! I hope that one day Coursera will have an advanced version of the class! (etc. Machine Learning II)
автор: Brian T S•
Professor Ng provides an extremely accessible overview of AI techniques. The math does seem a bit imposing, but anyone with a background in pre-calculus or higher should be able to "get it" if they sit down and work it out. I took a similar course at the University of Texas in the 90s and this was presented in a much more understandable manner. It might have been beneficial to work on a complete AI programming assignment at some point, or at least accomplish more of the coding, as most of the assignments required completion of the more trivial aspects of the technique. Also, I was a little surprised that search (tree traversal) was not addressed. Maybe that's too old hat, or covered in a graph course. There were some frustrations with Coursera not working correctly (unable to submit assignments due to broken URL forwarding, broken Latex rendering which is still not 100% working), but I really like the site when it works. High marks for Professor Ng!.
автор: Tom M•
I reviewed many courses before taking this one and I'm sure that I made the right choice. This course covers the underlying mathematics of how the various learning algorithms work. Understanding at that level is essential to designing and debugging machine learning systems rather than just applying rote techniques or blindly calling library functions in an ML framework.
I found parts of the course challenging as I'm not a great mathematician but I'm very glad I persevered. The pace and structure of the course were just right. I've just got one tiny gripe. I suffer from multiple hearing problems so at times I needed to turn on the captioning. The problem with automatically generated captions is that they struggle (i.e. get wrong) precisely the same words that I am struggling with, so for me at least, the captions were useless.
Overall, I would describe this as the defininitive 'must-do' course for anyone looking to get involved with ML applications.
автор: Joseph M•
Fantastic. Andrew Ng is a naturally charismatic teacher with a knack for anticipating issues which his students may encounter and assuaging them before they become sticking points for later understanding. By their nature, online courses cannot benefit from students asking questions of their instructors so it is doubly important that instructors be aware of areas which may confuse students and take anticipatory action to avoid this- this is only one of Ng's strengths. Beyond this, Ng is simply an enthusiastic instructor whose passion for his subject is contagious. He also conveys a genuine sense of understanding the student's process of coming to grips with more difficult portions, often explaining what has confused him before (though, given his expertise, one may wonder just how much these areas actually give him difficulty). All things considered, the biggest disappointment is that there are not more courses available with Ng as the instructor.
автор: Prithviraj C•
This was a very helpful introduction to machine learning. The instructor's explanations were very succinct but always rigorous. He provided insight wherever possible. There are some optional videos explaining much of the underlying mathematics - and even when there were topics where some of the math was beyond the course of the syllabus, professor Ng made it a point to provide references. (I especially appreciated this as PhD student in mathematics myself). I always think it is helpful before learning a new technique to ask 'How is this applied in real life?'. I can confidently say that Professor Ng made it a point to answer this question with every new topic that was introduced. The assignments and readings are very good at helping you become truly comfortable with the material taught. But they are never too tedious to be discouraging. Not only did this course teach me a lot - it also piqued my interest in the subject. 10/10, would recommend!
автор: Leonid B•
First of all - thank you very very much to Andrew Ng !!! There are some things which can be improved - as always but in general, I think it is perfect. I am a hight Energy Physicist and I have some reasonable good knowledge of mathematics. Some of the explanations are so good and so clear that I would use them while explaining quartum mechanics ))))))) and this is not a joke. I was inspired by Andrew Ng and had read partially (not entire) the Ph.D. thesis of Andrew Ng. Thank you one more time for all the algorithms you have explained here. Thanks to you I have: 1) my own spam classifier with my own dictionary - for this, I use bash and c++ library, 2) I have my own list of movies and I make these lists for friends :-) !!!!! 3) and finally, I have the vector components of my face and faces of my friends in the space of celebrities )))))). I wish you and your closes surrounding to keep healthy happy and focused on the things you like to do !!!!