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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

Оценки: 165,581
Рецензии: 42,411

О курсе

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....

Лучшие рецензии

11 июля 2021 г.

I've learned a lot from this machine learning course. A huge thanks to prof. Andrew for guiding me throughout this course, and also Coursera for providing me with such a platform to learn this course.

30 окт. 2017 г.

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

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476–500 из 10,000 отзывов о курсе Машинное обучение

автор: Janis K

5 февр. 2018 г.

Course "Machine Learning" cover all main topics of macine learning and describe algorithms very clearly. After the course you will feel that you are AI and machine learning expert. However, this is introductinary course and I believe seperate course can be created for every topic, algorithm and method covered here.

Course gives opportunity to solve real world problems. Octave was discovery for me and I find it much easier than R.

It was easier to follow the course because I had background in mathematics. You will need to use and understand matrices and vectors that are important to complete programming assignments. However course starts with mathematics and explain all the basics that will be used in this course.

Course was quite difficult for me, it was quite difficult to complete assignments in deadlines, I had no time to think more carefully about covered topics. I will do it now after a course.

All in all I strongly recomend this course if you are interested in machine learning and AI.

Thank you!

автор: Atul S

24 нояб. 2017 г.

Excellent course, was very interesting and helpful. As with any course, I have a few suggestions:

-- Why not develop the math in vector notation from the start? It would be easier for students to take a few minutes to understand basic matrix algebra, and then the cleaner vector formulation. All those summations, subscripts, superscripts, etc. are much more confusing to tease apart!

-- It would be helpful to have Andrew (or a tutor, for that matter) to write up the notes as a text. I, for one, would have happily paid (say) $10 for a PDF with a bibliography at the end!

-- As part of my "textbook" suggestion above, or as a standalone, it would help to have a small list with explanations of Octave functions used. That is, some of the built-ins, and also some of the more complicated ones (like fmcxxx). As an extra-credit exercise, you could also advise at the end of each assignment what to do to generalize our Octave code to make it even more useful (apart from vectorization), things to avoid, etc.

автор: Laimonas S

5 февр. 2017 г.

This was my second course in ML. I took it with the aim of gaining a deeper insight into some of the fundamental topics and I was not disappointed. The professor Andrew Ng teaches the concepts in a way that is easy to understand and reason about. I loved the pace and the way the material is structured. Quizes and programming exercises completed the lectures very well to give a more complete picture of the topic at hand. Actually some of the quizes and specifically programming exercises are quite challenging. This is actually a good thing as the lectures alone would make the course a bit boring and without any practical application examples.

I wish the videos were a bit better in terms of video / audio quality, so be prepared to ignore that aspect and just take the incredible knowledge that is given to you.

If programming exercises are too hard, do struggle through them and use the forums to solve them. It really helps you deepen the understanding of the concepts that are taught in the class.

автор: Julian C

26 дек. 2015 г.

This class was a great introduction to machine learning ideas and implementation. Prof. Ng does a really good job of not only showing you how to code up machine learning examples in MATLAB/Octave, but explaining the rationale behind them. If machine learning is as much an art as a science, then this is the "artistic" part, which is hard to find in a textbook.

However, I do kind of wish we had covered fewer topics, but in more detail. Mind you, I'm biased because I was a math major and want to see proofs for everything, but I would have really liked to see more of the details behind support vector machines and neural networks. If you're looking for that kind of thing, then it's probably best to do additional reading on your own.

Anyway, I still gave this class five stars because I have been searching for an introduction to machine learning that could give me a broad perspective (and share some wisdom of expertise) for a while now. I found it in Andrew Ng's machine learning class on Coursera.

автор: Philipp H S

6 окт. 2020 г.

Thank you very much to Andrew Ng and his Team for this very interesting course. It is clearly a great way to get a good background on Machine Learning.

Although I already finished my university degree in physics I learned many new things. Especially the discussions on applications and the best practice examples for improving machine learning systems are very insightfull.

The mathematics is on a level which allows to understand the basic principles of the algorithms well. The review quizes are generally well designed, only occasionally there are some problems with unclear formulations ("Is X a large value?", compared to what?)

It is a bit sad that the programming excercises are in matlab, python with numpy would be closer to industry aplications and there would be no need to always write "remeber that index 0 is index 1 in matlab". However this is only a minor point. In the general the excercises are very helpfull and were clearly designed with a lot of care.

In total, a really great course!

автор: Antonio C

1 апр. 2021 г.

I am currently a software engineer and just finished up this course. Going into this course, I only had a high level understanding of machine learning systems. I saw a review on here that said that Professor Yang did not go in depth as he maybe should have in regards to the math behind the algorithms. I would argue that if he would have gotten into the theoretical side of things, then this course would not be what it is today. Frankly, it comes down to your expectation for the course. I did not come into this course expecting to come out a machine learning expert. Rather, I went into this course with the sole purpose of getting exposure to such a technology and this course did a great job with that. With that said, I now have a deeper understanding of the capabilities of machine learning systems. I hope to apply this knowledge both in my personal ambitions and those of my employers. If you are on the fence about taking the course, do yourself a favor and go for it. Best of luck.

автор: Jason W

15 дек. 2016 г.

Professor Ng has been working with machine learning R&D for more than 10 years now and have seen the significant phase of evolution of this field before it gain its popularity. Undoubtedly, AI and ML is going to be ubiquitous and impactful in many creative forms in the coming decades and I'm very fortunate to not only gain such an in-depth intuition and understanding of the fundamentals of machine learning, but also to gain the confidence needed to articulate these concepts and theories with Prof. Ng's guidance. The difficulty of this course is average. Quizzes and programming exercises require solid understanding of the concepts and also a lot of patience (just because you don't understand a particular concept, doesn't mean you're dumb. Give it some time and perseverance and you will pull through). Thank you Prof Ng and Coursera for this course. I would recommend this course to be the first stepping stone if you're going to venture your life in to the world of Artificial Intelligence!

автор: Nimrod B

12 мая 2020 г.

I found the course very interesting and informative. I wanted to learn for a long time about Machine Learning, Neural Networks, Artificial intelligence etc. so the course in Coursera came in a good time during the COVID-19 quarantine. The videos are explained in a very good way by Dr. Ng. Slides are extremely useful. The question/quiz is helpful to digest the information and the programming exercises are done in a very good way in order to implement the acquired knowledge. I will probably spend more of my out-of-work time in Coursera in order to learn more about implementation of Artificial Intelligence and Deep Learning which are the next two subjects of interest. A final note: since I am using MatLab in my daily work as a researcher in academics I found no problem in implementing the exercises also in somewhat more advanced vectorized way from the earlier exercises. Many thanks for the excellent course and a nice interface for remote courses such as Coursera. Dr. Nimi (Nimrod) Bachar

автор: Ivan M

8 окт. 2019 г.

Andrew Ng is such a great person and teacher! This course is just pure gold and this is my first MOOC.

Andrew smoothly guides you through the most important concepts of machine learning, doing so, that you really understand things very well. He eaxplains pretty difficult things in easy way, generalize ideas very well, so, that you don't need to remember lots of things, but actually just understand principles.

Also, with his great experience in the area, he gives you super valuable advice on application of ML and prioritization of work. He knows what are the most important things to know, so you can trust him!

I was happy to learn everything and work on assignments thoroughly, which are of such a great quality!

Tests in each video and at the end of the topic are also great and help to check your understanding!

My life never would be the same :)

Andrew, thank you with all of my heart! Due to your work new generation of AI engineers is appearing!

Now, I will learn Deep Learning Specialization!

автор: Yemao

21 мая 2019 г.

This is the best course for machine learning beginners. The best. Andrew explained many fundamentals very well and it is not just one algorithm that he focused on but he wanted the students to understand how to debug and how to improve and optimise. These "strategic" stuff are probably more important than the hardcore "tactic" algorithm stuff because students will have a better understand about what they are learning and why they are learning this, more importantly what they shall be learning in the future. I would like to thank all the efforts from Andrew and other mentors on course for developing this fantastic course. If you really want to pick some bones from an egg i would say that probably provide a python version of this course would be brilliant. For the same course assignment, in matlab the codes should be this and the codes in python could be this...i know this will put so much much more work on the course developers but you know just a small suggestion. Thank you Andrew!!

автор: Azad A

14 нояб. 2021 г.

Before this class, I had no clues about machine learning, even though I have heard a lot about it. Since this has been my first exposure to machine learning, it is very easy to forget the details of various formulas, but the concepts of various topics are the most important aspects of the course that I won't forget, since if I ever want to know about the details I know where to go to have access to the formulas. Another comment is that it has taken me much longer time off-line to go through the materials to be able to do the assignments/quizzes and I should say that the amount of time invested for each week is a lot more than indicated in your course. If I knew that ahead of time I would have left the course for some other time. But I am very glad I was able to finish it. I enjoyed the course very much. Thank you for offering such a wonderful course and thank you very much Dr. Andrew Ng for all you have taught me (specially bringing up realistic examples throughout your course).

автор: Siddhartha S M

2 апр. 2020 г.

Profession Andrew NG has a quite indepth knowledge in the Field of Machine Learning and he covered almost all the topics in very great detail with the approach of creating basic building block of the Machine Learning of any individual. Although, sometimes I felt that professor deep dive into too much derivatives and mathematics but after completion of the course, I realized that all those stuff were necessary for creating a foundation of the subject.

The course content covers quite mathematics and consumes a lot of time but I felt it worth investing. I took more than the video time to complete the course because sometimes I had to google the terms and understand the basics first and then returned back to the course again to continue. This may be because I was novice for the field at the time of starting the course.

Thank you very much Professor Andrew NG for devoting your time and energy with full of compassion to share the knowledge and helping us building the basic understanding.

автор: Winson L

2 окт. 2018 г.

I graduated at UCL in London, my PhD was in Electronics Engineering, far from maths and computer science. Machine learning is a very interesting topic that I have always loved to explore. By coincidence I became a data scientist working in London where machine learning was needed. 2 years after I first come across Andrew Ng's coursera video lectures, I decided to finally go through all the modules and get the certificate. Not native to Octave, but I am glad that I have learned it for the assignments and now feel very comfortable on applying it. Today, I have finally completed this course, after spending many evenings late after work staying in my work office's meeting room to study. Many thanks to Andrew and all the examiners in this course. A special message to Andrew: I have recognised your appearances on TV and blog posts documenting artificial intelligence, I wish you every success, and I secretly wish that one day we would cross paths with each other.

All the best. Winson Lam

автор: Humberto F F

17 авг. 2015 г.

This course is an opportunity to get acquainted with several machine learning techniques, including linear regression, logistic regression, Support Vector Machines (SVM), anomaly detection, non-supervised learning (clustering, K-means, etc), recommendation systems and very interesting discussions about batch/mini-batch versus stochastic learning and large-scale learning systems. It does not require a deep knowledge in algebra and calculus (although a solid background in mathematics surely helps a lot) and progresses in a logical manner from easy, standard techniques to advanced ones.

If you are new in this realm, this course is comprehensive enough to make you confident to design your own customized algorithms. If you have some experience, you can consolidate your knowledge and benefit from the tips the instructor gives throughout the course. I've been dealing with adaptive filtering for some years and I can say I've enjoyed this course so much. I definitely recommend it!

автор: Wahyu G

15 дек. 2017 г.

This course is really really really amazing for me! Andrew Ng is a great lecturer. There are 2 main parts here, the maths and the intuition. Most of the time, the class talks about the intuition and the reasoning, i love it. The reason behind that is you can take a more advanced course about machine learning or deep learning afterwards with a good intuition about the algorithm. But, the math is not so little, too.

There is also the programming assignment which really really helps. See your code works is one of the best feelings, even you dont build it from the 'really scratch'. The hardest part in this course i think is in the Neural Network and SVM part, but once you've past through that, trust me, you'll pass along and enjoy the class.

100% will recommend it to my friends. Speak about myself, I am not a cs student but i think i have a little bit of confidence now.

In the last video, i'm so touched. Thank you Andrew and team, definitely going to take Deep Learning specialization.

автор: P S R

16 авг. 2017 г.

Fundamentals well explained, solid programming exercises complement the theory giving us an opportunity to see the theory in live implementation. Contemporary solutions like recommendation systems, e-mail spam, image recognition and long standing regression/classification techniques are well balanced. Advice on practical implementation of ML applications is the highlight. Over all it is well designed and delivered. However, it approaches more from mathematical/engineering stand point, whereas in business world it is approached more from statistical analysis perspective using co-relation, R-square, p-values, error function following normal distribution etc. Some linkages between the two approaches may help us become more productive at real life work. Almost entire course focused on classification problems, except the first exercise that deals with house price forecasting. May be few examples of regression with the same algorithms also can help matching the needs of enterprises.

автор: Iain M

6 сент. 2015 г.

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

4 янв. 2017 г.

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

8 окт. 2016 г.

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

30 мая 2020 г.

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.

автор: shockwave2000

10 янв. 2020 г.

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

26 янв. 2018 г.

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

30 апр. 2020 г.

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

14 июня 2020 г.

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

11 янв. 2019 г.

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 :)