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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

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Оценки: 149,527
Рецензии: 38,097

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

CC

Jun 20, 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

OK

Apr 18, 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

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

автор: Dr. H d l H G

Jul 01, 2020

I am impressed by the tender and empathic way that Andrew uses to explain the material. It is a pleasure to listen to him and learn with him! In addition I am very thankful to the people that put continuous effort to improve the programming exercises and answer the questions of the students, so that we can walk through the exercises focusing on the main concepts of the course - you all made a wonderful job!

The only thing I would suggest is that you find a way to offer lower prices to students in low-income families. 80 dollars may be perfectly affordable for many Europeans like myself but it would be a lot of money for very capable students in other situations in the world.

Apart from that it is almost unbelievable that these algorithms (videos and code) are offered for free to the whole world over the internet - I feel very lucky that someone recommended this course to me!

автор: Cameron F

Apr 01, 2018

I graduated from college last year with a math degree without a ton of programming experience; I'm working as a developer now and I've gained a lot of confidence with my programming ability, but I hadn't quite found a good way to satisfy my curiosity about machine learning algorithms. This course was taught incredibly well--relatively accessible while being comprehensive enough to satisfy my curiosity in situations where I wanted a bit of a look behind the curtain at how these things really worked (which is the issue with many articles/tutorials online--either they plop down equations without giving you a shred of intuition about how they work or they go right into the weeds). The workload was of course lighter than it would be in a typical college course, but honestly I was very pleasantly surprised by how much I learned and was able to retain in this course. Thank you!

автор: Praveen S

May 26, 2020

Andrew NG's style of teaching is so charming and all the lectures are extremely well thought out and structured. The course load per week was also perfectly designed in terms of the time it took. The quizzes and the programming assignments also gave a lot of insight on how to program machine learning applications. This course is perfect for beginners in machine learning. Andrew NG teaches everything from the start and doesn't dwell to much on advanced math and covers the requisite math pretty well (but for those who want to go in depth, he offers an insight into the concept behind it so that you can google it for yourself). If you are interested in data science and looking to start a career in the same, this course is for you. Thank you Andrew for introducing the vast and wonderful world of machine learning and data science to me and countless people all over the world!

автор: Guru S T

Jun 24, 2017

I thoroughly enjoyed Dr. Andrew Ng's lectures. He mastered the art of teaching complicated concepts (stat and linear algebra stuff, ML is notorious for) in a simple and clear way. I loved the graphs, 3d visualizations throughout the course material. I specifically loved the real-life neural networks example (a simplified version of self driving car video) shown in one of the lectures. This course is designed to equip students with not only the foundations of machine learning but to ignite a genuine interest and love for the field, which is a really important goal for any course. The programming assignments, review materials and discussion forums, all provide tremendous learning around the concepts taught in lectures. I would suggest every student taking the course to thoroughly understand course materials even at the cost of extending your course session to a later one.

автор: L'EMIR O C

Jul 19, 2017

Wonderful course! Andrew Ng has a way of helping you grasp the intuition of Machine Learning material whilst giving substantial leads into the more technical aspects. His vocabulary appeals both to the aspiring statistician and the amateur enthusiast. One ends with a thorough picture of the subject, from motivations of a Machine Learning problem, to the mathematics of regression & neural networks, to algorithmic implementation with good habits (clean code, vectorization, parallelization, etc.), to practical advice on error analysis & effort distribution. The 'coding' homework is gratifying : for pedagogical bits of code entered, we are rewarded with a graphical display of Machine Learning at work. Furthermore, every technical concept is introduced or illustrated by a clear, contemporary real-life situation or simple 2D example that sticks with you. Thank you Andrew Ng!

автор: Herman A

May 12, 2016

Taking this class was quite a challenge as I took many different classes on different platforms in preparation for my going to grad school.

Professor Ng's class is, pedagogically, a very masterfully planned class. His simple but meaningful way of teaching made digestion of new information easy. He also used media to aid in teaching in an expert way, like using different colors to help keep track of variables, and using clear and meaningful diagrams.

And also, in behalf of the TAs and mentors, I'd like Professor Ng to know that they are a great asset. Without their help, most notably in the form of programming assignment tutorials and test cases, this class would have been much harder. Their step by step guidance to those of us unfamiliar with R or computer programming was essential, all while upholding the code of conduct. The TAs and professor Ng make a formidable team.

автор: Steve H

Sep 14, 2019

Beautifully organized overview of basic concepts and applications in machine learning. The instructor is skilled at communicating an intuitive understanding of the algorithms and the essentials of how they work. He covers the key ideas while avoiding getting bogged down into mathematical and technical complexities. He provides diverse examples showing how the ideas can be applied to big datasets, images, spam filtering, robotics, website traffic, etc. The course is enjoyable and entertaining.

Most of the math is at the linear algebra level, with occasional mention of calculus. Students who are very comfortable with Matlab/Octave and linear algebra will have no trouble following the course and completing the programming assignments. Students who are unfamiliar with linear algebra will probably struggle and should brush up on those skills before starting the course.

автор: Ahmed A

Sep 13, 2018

The course was extremely beneficial for me.

The tempo of the classes was optimal. Not super fast that you miss details and also not super slow that you lose attention from boredom.

The content is very well selected. In a nutshell a student can get a very good broad view on various topics. I would call it "To the point".

The programing excercises were the best thing because it gave me an opportunity to apply what I theoritically understood and play with the scripts a bit with debugging mode to deeply understand the concepts being executed in front of me. That helped me verifying my understanding and made it hard to forget what I learned.

I have to admit that some of the content I already read about before but it wasn't until going through the course that I started to deeply understand what I read before. You know I had a lot of "Aaaaah okay that explains how it works" :-)

автор: Heiko S

Nov 14, 2016

Andrew, a big 'thank you' for teaching this class! Even though I DON'T consider me as an (real) expert in ML now, I got a thorough understanding of many many topics of ML. Prepared with an arsenal of algorithms and a lot of great advice I feel ready for take-off. The course has been teached in a perfect order, explaining not only the mathematical underpinnings (luckily not in depth) but focusing on the intuitions, rationale and use-cases behind the different algorithms. The many given examples (lecture and ready-to-run-code in exercise) have been very motivating and inspiring.

Some exercises turned out to be a bit time-consuming when trying to implement them in vectorized form. The catchiest part has been installing Octave (4.0.3) on OSX. I strongly recommend to go with the virtualbox/vagrant/xquartz based installation (see resources). So that's it. Thank you again!

автор: saurabh k

Jan 26, 2017

I am Wondering Before I Don't know how to Implement Recommendation Features and Suggest Products On Home Page as Much Targeting the User We Have done Lots of Effort and Design More Complex Database tables and Structure but i but we did not Mining The Correct Data as per Users Views and Choice Thanks To This Course it Help me more and more in Redefining my Programming Technique and Think To Move through the Artificial Neural Networks and NLP For Getting More Relevant Search dataset collection For Targeting More and more users and thank to you also Coursera team and Respected Prof. Andrew ng He is Delivering an very Nice Tutorial Session and Bunch of Assignments which is actual relevant and Genuine Example which are Understandable to any new student who Join this course and Thank you for this course forum moderator also they are really nice .

Thank You

Saurabh Kashyap

автор: Guillermo A

Oct 07, 2018

The course is really well structured and easy to follow if you have basic knowledge in any language programming language. Basic concepts of probability and calculus might also be handy for understanding some of the proofs or derivations, but are not needed at all to follow the course. I particularly liked how you get to implement some ML algorithms yourself and see them in action. It is also worth mentioning that the programming exercises just prompt you to complete the key parts for each assignment, so you don't have to code everything from scratch. For example, for the problems, you are usually given the functions to load and visualize the results, and your task is to implement just the logic for the task. One thing that could be improved is that after week 6-7, there are no lecture summaries after each video, which I found very helpful during the first weeks.

автор: Aleksandar I

Jun 18, 2018

I am primarily working as a PhD student in Neuroscience. I came to this course with mostly biology background and some decent, although not extensive knowledge in maths and stats. The course has been absolutely fantastic. All the topics were very well introduced and developed. I was really astonished and pleased how well Andrew Ng was able to explain the concepts clearly and comprehensively, without sacrificing rigour or depth. He goes into enough detail in the math to give you a thorough theoretical grounding without assuming tons of knowledge and for the more advanced students points out relevant topics they could explore further if need be. I think this is an excellent introduction to machine learning and I would highly recommend it to any student in Computational Neuroscience/Biology or anybody really who wants to know more about ML. Andrew Ng is the best!

автор: Dante K

Aug 24, 2020

"Why should I learn programming? What is it useful for?". This, this is what it is useful for, regardless of the area you're working on. While I don't agree with Octave/MATLAB being the most beginner-friendly platform, when Python is the prefered language for machine learning, Andrew Ng succeeds in giving a great overview of most areas of machine learning and how easy it can be to begin playing around with it. He goes on not only to explain how to use these algorithms, but also teaches the important metrics which, while not fun or glamorous to talk about, can save you tons of money and time and allow you to get much better performance in your applications. Overall, I'll reccommend this course to absolutely everyone who wants to learn Machine Learning, even to people who don't know how to program and were asking themselves "Why learn and where should I start?"

автор: Andrew W

Apr 24, 2020

Extremely enjoyable course. Professor Ng really takes the content down to an understandable level by focussing on conceptual ideas over the mathematics (which is still interesting, though would probably be better learnt independently or at universities). As a maths student myself, I got a lot of value from this course's introduction to all the main algorithms used in machine learning and how to best apply these tools. Although the course is quite introductory in terms of difficulty of the maths/programming exercises, it is very comprehensive and well worth the time for any beginner with a decent mathematical background (some knowledge of 1st year university maths). I also liked how MATLAB was used to simplify the programming and it was obvious a lot of effort had gone into writing the MATLAB guide scripts. Would definitely recommend this course to my friends.

автор: Filippo R

Sep 20, 2018

A lot of effort, passion, and time went into the creation of this course. The fact that it is made freely available is a testament to the dedication of the persons involved to share and spread knowledge for the larger benefit of society. The course material is presented slowly, with many repetitions and many examples, so that literally everyone with even minimum higher education and programming skills can potentially take this class. If you have good upper level education and programming knowledge you can easily follow the course at 2x speed on the videos. The quizzes and programming tasks are easy but still make you reflect on the main parts of the curriculum. In the end, the course provides a great overview of the main tools of machine learning and presents some interesting applications, making it a perfect starting point for your education in this field.

автор: Mouhamadou M S

Aug 03, 2018

Going through the whole process, I really think that this course is a strong introduction (and sometimes more than just an introduction) to the machine learning field. Andrew gived to us many advices about the applicabilty of the learned concepts that could help anyone to get more confident while having to conceptualize the ML problems and to implement the adequate machine learning systems. However, I would have appreciated that some of the scripts that were already implemented would have been part of the exercises to help us enlarging the technical skills gained using Octave/Matlab. Specifically, some of the plotting scripts or the script to implement the pre-processing of the e-mail text (spam classifier exercise) could have been opened to some stepwise implement to participants. Nevertheless, my general impression of the course is extremely positive.

автор: Sebastian

Oct 17, 2020

Hello, I just want to say that this course was extremely interesting. I am a phd student in physics and tried to find a way to learn machine learning by myself. Luckely, one of my colleagues recommended this course and so I started and enjoyed it very much. I think that I learned quite a lot and are now eager to apply this knowledge to my own projects and finally also help other people. For me this course was the perfect starting point and I am sure that I will remember it for a very long time. I also would like to thank you that you provided all this material for free. Although I paid for the certificate I know a lot of student who might not have the ability to pay money for special books or so. I hope that I can also recommend this course to a lot of future students and wish you all the best. Best regards and thanks for the beautiful course! Sebastian

автор: Pavel T

Aug 23, 2017

I wanted to say how Iam grateful for this oppurtunity to take this outstanding course. Honestly, that was best class I've been through for all my life. Proffesor Andrew Ng has really wonderfull style of teaching, its a big honour for me being your student. I wish someday I will be able be your student in real life. I understood every aspect of this class, all explanations were clear and repeating. It made me clear up and feel all maths and technicalities. Programming exercises were great, I saw how algorithms are working in my PC downrightby my commands. Andrew Ng is a machine learning developer, so his stories of live examples were awesome.

Thanks to all mentors. Thank you for responses and all your support. Special thanks to Tom Mosher, he resides this course and his support means a lot for all community, Tom is great in making tutorials.

автор: Vladimir B

Feb 11, 2018

I found this course has a good pace, I feel like I've learnt a lot but at the same time nothing was rushed and I never felt like I was struggling to understand. I think many of the examples used were well considered and give you confidence that you could actually create your own machine learning algorithm for your own application. My only criticism would be with some of the programming assignments, where I would sometimes spend more time trying to understand the existing code (so that mine would be using the right matrix indices for example) than actually doing the maths and learning about the intended subject. I don't know how you could get around this problem without requiring the student to write the whole program themselves though, which I certainly would not prefer! Maybe this is an inevitable consequence.

Thanks Andrew I really enjoyed your course!

автор: Skltfz S

Nov 25, 2016

i learnt something, but i can never able to use it. i have no idea, anyway, ML is just still having the real application for the command development field in the business now. for example like me, working in software development firm and medicare company before, none of them really applied the machine learning theory. although, they have certain value if applied, for example i saw many of them trying to convert their business to big data business, however they never use the big data correctly, assuming applied a machine learning in order to predict the potential customer response of a single product, it looks fun. anyway, fact is fact, currently the business is still a relatively low technical level business. everything is report, linear, and simple ( i will not say unfortunately, at least i have time to write this comment when i am actually working)

автор: David K

Feb 05, 2020

I really enjoyed this course, Andrews bottom up style gives you enough detail to understand the key concepts in machine learning and lay the foundations for further training. Andrew shields you from the hard maths but creates an understanding of the intuition that makes you feel you know the maths.

I would have preferred python for the code but in my view the value was in the wider machine learning training as opposed to coding. That said it was very satisfying to code an algorithm and see it work, this level of detail helps to understand libraries like scikit-learn.

My motivation for this course was to understand what’s under the hood of machine learning and this course delivered that. Overall, this is a great course and I learned an enormous amount of information, its the perfect compliant to the more practical and hands-on courses I am taking.

автор: Syed M I

Apr 09, 2019

I was wondering why we are being forced to use Matlab/Octave in a time when everyone that i know uses Python. I realized it's wisdom in the first week itself.

Mr Andrew has designed the course very cleverly, he is aware to teach things in the least time, in the easiest way possible. He boils down his theory to few lines of formula which can be coded in a few minutes. Throughout the lecture he keeps on giving hints on how to structure the code for that algorithm.

In the programming assignments also he makes sure that you only get to apply your brain where it is needed and he has himself pre-written the labour intensive part of the code. However we can easily analyse the whole functioning of the code by merely glancing at the portions which we have not written.

I am about to finish week 4. He presents this subject like it's a standard 10 school subject.

автор: Charles B

Jun 08, 2018

Andrew Ng presents the major machine learning algorithms, explaining them from the ground up. The level of detail in this courses was great from my point of view: it skips some of the calculus (e.g. doesn't show working for most derivates), but does explain the linear algebra involved. This is important because the parallel processing needed to implement machine learning efficiently relies on use of matrices and vectors - as done in the coding assignments in this course, which were a lot of fun to solve.

The assignments are in Matlab, which isn't a mainstream programming language; but I found it easy to learn and it does lend itself well to applying vectorisation techniques. Overall, I found this course really engaging and I now understand details of algorithms like linear/logistic regression and neural networks that I'd previously taken for granted.