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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

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Оценки: 151,549
Рецензии: 38,667

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

AQ
2 мар. 2018 г.

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

CS
22 мая 2020 г.

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.

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551–575 из 10,000 отзывов о курсе Машинное обучение

автор: Christopher P

22 июля 2018 г.

Excellent overview of Machine Learning. The goal of this course is to explain the main ML algorithms from a practical, intuitive perspective. This is accomplished using real-world examples, and the key ideas and techniques are reinforced by Matlab/Octave exercises. [No prior Matlab/Octave experience is assumed, and in fact, this course allows one to pick up the basics easily, if needed.] The key mathematical results that underlie the algorithms are presented, but there are no rigorous derivations or proofs. Some differential calculus and linear algebra background would be helpful, but it really isn't necessary to do well in the course, and to apply the various algorithms successfully. Finally, I found the notation consistent and clear throughout the course, and this helped to tie things together.

автор: Karan R

21 июня 2016 г.

The best course I've taken on Coursera so far. This was the first ever course offered on Coursera, by the founder himself, Andrew Ng. He's a great instructor, covering topics right from the ground to the sky. I'd say the implementation is in OCTAVE instead of popular languages like R/Python, which could have improved upon a lot for learners. But since Andrew has focussed this course upon beginners, I think OCTAVE is apt.

The assignments were relatively easy as most of the implementation (ground work) was done, only the main functions were to be implemented. But yes I learnt a lot from the way the assignments are designed. You create a digit recogniser just by being through this course.

I would recommend it to all learners who are beginning with Machine Learning or Data Sciences to take up this course.

автор: Denis O

16 февр. 2019 г.

Great introduction to Machine Learning.

It gave me exactly what I was hoping for: at the end of the course, I feel like I can look at a typical machine-learning / AI / neural network program and understand how it might work (of course, a specific program mightn#t work that way, but I#d know one way that it could work, and the type of results, predictions and flaws to expect.

The programming exercises were very helpful because they forces us to think and to refresh our knowledge of linear algebra. I would probably have made them a little bit harder - not that they were easy for me at all !!! - in the sense of ensuring that we had to always program the critical code for the key topic of a given lesson. But maybe that#s not realistic.

The lecturer is phenomenal - very clear, very precise, very engaging.

автор: Abdullah S

25 окт. 2017 г.

Just Excellent, everything about this course is just fantastic, beginning with Prof.Andrew, passing with his passion for the subject and his motivation to really make you understand everyword he says, he is keen on delivering all this expertise and this alone is a fine quality, the course is well organized and the quizzes and programing assignements are to the point and are a very good exercise, I just felt the course needed 2 small videos one addresssing the differences between linear regression, logistic regression, SVMs and Neural Networks and another video exciting people by a small example of machine learning on self-driving cars (very small programming assignments to help excite people and give them an-overall idea)

again Thanks to Prof.Andrew and all who helped me find my hobby and passion :D

автор: Xiang L

10 июля 2017 г.

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. It was so great and very helpful of implementation.

автор: Lokesh N

15 июня 2020 г.

Immensely helped me to start off with Machine Learning. Every concept is deliberated beautifully by the instructor. He has great expertise in the field and understands especially on how to teach or guide the novices and think from their perspective. In this regard he delivers what is concretely required. This course bridges the gap between applicability of the algorithms and mathematics behind it without deeply diving into the latter. If someone isn't well versed with Linear Algebra,Calculus and Statistics they might find themselves impatient for superficial explanation in this regard. This course would have been ideal if it's worked out with the current language in demand and bit more practical. Overall, In my opinion I would definitely recommend this course for all the beginners.

Heartfelt thanks!

автор: Divakaran K

21 окт. 2017 г.

Andrew Ng's teaching methods go a long way in imprinting the concepts very clearly in our minds and makes us understand each and every concept perfectly before moving on to the next. Perhaps he is THE best instructor out there in the field of machine learning. The exercises have been designed in an absolutely error-free way that helps us to quickly learn from them without any hassle. That, combined with the small small instances where Andrew talks about his practical experiences makes us more confident in our course progress.

Just a small suggestion : It would be great if Andrew took these classes with python too. Maybe you can add it as additional(optional) exercises in the lectures. It will go a long way in making everyone feel that they are actually coding industry-level stuff with this course.

автор: Mohamed H

22 авг. 2019 г.

Very well taught class by Prof. Andrew Ng, he explains everything in a really cool way by showing the benefits and applications of a concept first before diving deep into the formulas. I read some reviews mentioning the high focus on math but I experienced it differently. Coming from a Computer Science background it was easy to follow the mathematical part even if you're not very strong in math like myself. You just need a little linear algebra to follow and understand everything, as the hard proofs aren't presented. One last thing, maybe this course should be in python now, however, Matlab was very easy to start and it gives you more of the theoretical understanding compared with python. This is due to the intuitive mathematical syntax of Matlab which is very similar to the mathematical formulas.

автор: Paul L

16 апр. 2020 г.

I am a student in engineering and i had a lot of notion of Machine Learning already. I learned in class and myself so i already knew the majority of the concepts. But i have to say, i am glad to have taken this class because i realised that some algorithms i thought i knew were way better explained and that finally, i didn't know them well. Also, after having done several Machine Learning projects for my school, i ended up using libraries without knowing exactly what the algorithm did. Now I know.

Only "negative" point: maybe the using Matlab in the course is not such a good way to practice nowadays. Everybody use Python or R today, so i think it would be good to adapt this class by letting the student choosing the language (even if i realise it demands a lot of work to rewrite all the exercices).

автор: Alex K

27 февр. 2019 г.

Brilliant introduction to machine learning. Shows the student that there is *so* much more to the field than just the fancy, hot topic things like neural networks and self driving cars. There really is a simpler and fundamental grounding to these things and it turns out that we can achieve amazing results with just a bit of clever matrix algebra and calculus.

Very good pace - not too fast, but can be sped up using the video controls. Well structured - Earlier topics are referred back to later in the course so that you're continuously reinforcing knowledge you've already gained. The programming exercises made me think, but weren't so challenging that I got frustrated or stuck.

Thanks Andrew hopefully when I'm further ahead in the field I'll be telling people this is what really got me started!

-Alex

автор: Anas H

19 окт. 2018 г.

What's great about this course is that it not only teaches about the theoretical aspects of Machine Learning, but it also gives you a chance to get your hands dirty and apply what you learn on real life applications. The course provides programming exercises which are a great way to demonstrate some of the cool stuff you could build with the knowledge that you gain throughout the course. What's more, the way the material is provided makes it possible for inexperienced people who have no background in Machine Learning or Mathematics to get a pretty good understanding of the way the algorithms work without delving too deeply into their most intimate details. I thoroughly enjoyed every part of this course, and I would definitely recommend it to those who are just starting out with Machine Learning.

автор: Brandon B

10 сент. 2017 г.

What I loved about this course is that Andrew jumps right into the details without a lot of fluff that other lecturers use to fill the time. The mini-quizzes that pop up during the lecture videos help to solidify the material being learned. The graded quizzes are just challenging enough to make sure you did actually comprehend the lectures. The programming assignments were fun, challenging, although I did notice that there was quite a bit of hand-holding when it came to setting up the algorithms and executing them. A lot of the heavy lifting was already done, but it was just enough to get the point across by allowing the student to think about how to implement the main algorithm being studied. Overall, I do recommend this course to anyone interested in getting started with machine learning!

автор: Swagata C P

7 янв. 2019 г.

Really good course.... Enjoyed learning this although Machine learning being a alien topic to me. Understood every chapter, lessons and all the topic. It helped me to realise that machine learning is something in which I can do my career. As it is just the start of my career this realisation was important for me.....Only one thing that I didn't understood from the course is that how to make our own features for any image input or so, but I am sure that it has cleared my other topics so much that I can get the information from other sources as well and will be able to understand it quickly from the point of view that any one should have for a particular problem, which I learned from this course.... Thankyou for the course and for making such knowledge available to us just at the click of a mouse

автор: Vineet J C

19 сент. 2017 г.

I learned a lot and thoroughly enjoyed this course! I'm so glad I took this up in my summer vacations and this course helped my curiosity. I wish to learn more and more and I might as well take up the Deep Learning course next! Thank you so much to Andrew Ng for being such a great teacher and explaining concepts well to make sure students get the intuition behind how things work. It's my passion to learn something new and through this course I've learned something new and something I am very much interested in. I am glad to be able to feel that I have mastery over some knowledge in Machine Learning! I'd suggest this course to anyone interested in Computer Science and AI etc. It was worth my time and surely will be if anyone else takes this course up. Thank you once again Andrew Ng and Coursera!

автор: keshav t

21 мая 2020 г.

A very well framed course. A great pool of knowledge imparted by Andrew Sir.

The course basicaaly covers the concepts of different machine learning techniques say Linear Regression, Logistic ,Artificial Neural Network, SVM etc.

Give an insight how these ML models actually work , formulas and equations used.

The programming assignments in this course basically asks us to implements these formulas and functions and the rest of the things are precoded. I wish this course to be more based on practical experience and there should be a project also before the course completion.

The course is a great start for all those who want the in depth knowledge about machine learning.

Thank you Coursera team for hosting this course.

Thank you Stanford University, Andrew Sir and all the Mentors

Regards,

Keshav T.

автор: Matt P

23 авг. 2019 г.

I thought Andrew's explanations of the course material were outstanding. I really felt like it was just the right amount of time devoted to each topic and that I had developed a solid intuition after watching each video and participating in the embedded understanding check. In fact, I thought these checks were very well thought out and forced me to apply some deep reasoning into the material. The one thing I would have liked was even more problems to work through to reinforce my understanding. Maybe an optional "additional problems" for each section. Also, I would have enjoyed working on a programming assignment for the large scale machine learning module. That said, I understand that additional work may not be in line with the scope/goal of this course, so overall I thought it was excellent.

автор: Daniel G

20 янв. 2018 г.

The only valuable constructive criticism I have is that for the first few "weeks" (I completed the class in about 20 days), I didn't know about the programming exercise tutorials, and while I really struggled to complete them because of this, I also really got a solid understanding of what was going on, and how valuable dimensional analysis is for vectorized solutions. Once I found the tutorials, the programming assignments became trivially easy, as Tom and the other's writings, while intentionally not explicitly copy-pastable, give very clear guidance on how to solve the problems. As a result, I am almost positive I'm going to go back and re-implement back-prop from scratch because I don't feel I have a solid grasp on it. Great course overall, can't wait to get more involved in this field.

автор: Kyle L

12 авг. 2017 г.

This class is a thorough and enjoyable introduction to Machine Learning. While I have had prior experience with statistics, linear algebra, and Matlab, this course helped me completely reimagine how one could creatively explore the marriage between these various mathematical concepts that I learned about individually in school. As a co-founder of Coursera, Andrew Ng puts forth a brilliant instruction package which includes: introductions to Matlab, linear algebra, and statistics; well-paced lectures on machine learning concepts and workflow considerations; and insightfully designed coding projects that enable students to practice machine learning rather than syntactical debugging. Whether you are a beginner or are experienced in Machine Learning, I highly recommend giving this course a go.

автор: Anmol S

25 мар. 2017 г.

The course gives a very clear foundation for machine learning. The course is by no means complete or comprehensive, but given the vastness of the subject that is not possible either. A substantial amount is covered every week in the lectures, and completing the assignments definitely made me feel like I learned something. The quizzes are well crafted and gauge not just cursory, but deeper understanding of concepts. The instructor, Prof. Ng makes the lectures very engaging, and teaches everything in a structured manner.

The only improvements this course needs is with regards to comprehensiveness. For students who wish to delve even deeper into the concepts mathematically, there should be more optional lectures. Lacking that, a list of further reference material will certainly be of big help.

автор: Emilian M

13 сент. 2015 г.

Excellent course, very well explained with a lot of examples and intuitions. The practical exercises are very well structured to make you think of what you were taught in the class. The teacher explains every part very well with a lot of examples and real case scenarios. If you want to start learning artificial intelligence this is the place to be. There are many algorithms you are taught and many good practices that should be applied to this algorithms to make them effective and performant. Something nice to have, would be the written courses or some paperback with the main ideas, but the videos are downloadable which fills this gap pretty good. Overall this is a nice course and you definitely should attend it if you're new to machine learning or want to better consolidate your knowledge.

автор: Sunil K S

25 июля 2019 г.

If there is any course that boosts the confidence of beginners in ML, it is THIS. Challenging topics were taught in a very lucid and organized manner. Many thanks to Prof Andrew Ng and team behind it. Questions were carefully designed in programming assignments so as to guide the student at every aspect of learning. Hints have surely helped me to think in right direction. Cracking the problems all by himself/herself is what boosts the confidence and clarification of queries is important aspect of this. A big thanks to mentors. Replies were quick and to the point. Anyone without any prior knowledge of programming and /or mathematics can easily take the course and complete it with such a well organize platform. What all students need is continuity. Thanks coursera, Prof Andrew Ng and team.

автор: Sugandh J

2 янв. 2019 г.

This course is the first properly organised and designed study material for me to learn and transition into Data Science profession. The course is very very well designed and covers a range of topics. The quizzes and programming assignments have been tough to pass for some of the modules and that gives the course a feel of complete learning experience at par with classroom learning experience. The examples in lectures and programming assignments are from real world applications and provides a great feeling to know how some of the things I use actually work behind the scenes, like recommender systems for movies/products, image to text conversion systems, image compresionm market segmentation etc. I would highly recommend this course to anyone who wants to learn/implement machine learning.

автор: Vishal J

3 февр. 2017 г.

Excellent course and great content. The course videos were well presented and were able to explain the subject matter well. The course covered most of the key topics in ML. Prof Andrew has done a fantastic job in creating content, content delivery and setting up the programming exercises. The course Mentors were also very helpful and always available.

Couple of suggestions: 1. A lot of complexity exists in creating the data-sets and defining /loading the variables. There should be one topic on data-set creation and defining the key variables (X,y, Theta).

2. I feel the difficulty level of the course should be increased. It is a fairly easy course from a programming point of view as most of the code is given or strongly hinted in the resources or by Mentors. The course needs to be "harder:.

автор: Martin S

26 окт. 2016 г.

This was an excellent course. I want to thank Professor Ng and his team for putting together a very digestible and intuitive introduction to machine learning. I wish more introductory courses were like this. Now I feel equipped to tackle my own problems and read advanced material on my own.

The strongest point of the course is that the essence and purpose of the algorithms are explained intuitively. Sure, advanced algorithms might changed the way they do regularization, their kernel, or other parameters, but you now know what it means. Also the debugging and diagnostics section is very intuitive and easy to digest.

If you are interested in machine learning, this is the course to take.

My only minor feedback would be to include a decision tree and ensemble section, due to their popularity.

автор: fanyfan

7 дек. 2015 г.

At the beginning, I thought it would be hard for a starter in ML; however, during the process, I found the course setting is easy to follow, especially the quiz, which help me master the key point of each class. And the programming exercises give me the intuition in applying each individual ML skills. I think the course settings are convenient and practical. So after finishing the course, I have excuse to recommend other ML beginners to join the course, which will help to build a skeleton of the knowledge about the concept, algorithm, and skills in ML. AND thank Andrew and Coursera for giving such an practical guidance.

The suggestion at last what I want to append is providing some further courses or future direction after this course. However, maybe I can get them from the course forum.