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Отзывы учащихся о курсе Машинное обучение от партнера Стэнфордский университет

4.9
звезд
Оценки: 136,417
Рецензии: 34,221

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

SS

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

RR

May 19, 2019

This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.\n\nA big thank you for spending so many hours creating this course.

Фильтр по:

76–100 из 10,000 отзывов о курсе Машинное обучение

автор: Yash B

May 25, 2019

This course was very well taught. There was a impressive focus on the basics and fundamentals of each topic. The lecture slides encapsulates the topics well and thus there was no such need of making my own notes which speeded up the learning process ;).

автор: Sunesh P R S

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

автор: claire.hou0701@gmail.com

May 18, 2019

sehr gut!

автор: Ganesh K A

May 16, 2019

If it was in python, then it would have got 5 star from me.

автор: トミー ペ

Feb 03, 2019

This course was very difficult, coming from a non-math/matlab background, but did teach me a heck ton about the world of machine learning, for which I am eternally grateful. Life got in the way big time, and it took a lot of time and energy to complete the programming exercises. There was also a lot I didn't understand, and I did wish there was maybe another week of getting used to certain concepts, particularly maths issues like double summing. I appreciate that this would complicate things though. I found that I am not geared towards the forums - my learning style involves conversation and not really experimenting on my own (which I can do once I understand a concept). As helpful as the mentors were, only relying on the forums with my time schedule meant that that taking this course dragged on longer than I would have liked. I also got a bit overwhelmed by the lack of centralised information. I know that it would require a complete overhaul to sort such out, but it did make looking up information time-consuming. Nevertheless, I am grateful for all that I learnt, and appreciate that I plunged into the deep end. I don't understand everything, and of course a little knowledge is a dangerous thing, but I know enough to know what to refer to should I ever need ML in my next job. Thank you.

автор: Jerome P

Mar 30, 2018

Good introduction course, giving an overview of machine learning algorithms and some methodology. Off course a lot can be added, but it's a good start for people with little to no knowledge or experience in this field. A few points that could be improved: I would like to have better material support for each section. Marked-up slides are not a great support for reviewing the different sections afterwards.

It would not hurt to provide a little bit more theoretical background and justification when covering the different algorithms. Andrew Ng almost apologizes when going into mathematical equations, but this is fundamental to machine learning.

quiz assignments are rather easy. They could be a little more challenging

I would rather have the programming assignment using R or python than Matlab.

But still a decent course overall I think.

автор: Mohammad G

Apr 24, 2020

It is a good course that covers essential topics related to Machine learning. But unfortunately, the quality of videos and sound are not satisfying. Besides, there are lots of mistakes in videos, notations, and even in programming assignments. It is time-consuming to check Errata for each week to find out which part has mistakes!! It is even got worse when I was in the middle of a programming assignment and I confused by the WRONG algorithms in the question and notation in the videos. In programming assignment 4, it took a week when I finally realized my mistake occurred because of the wrong algorithm in the videos and the assignment. I found out these problems confused all the students and its evidence is the comments in the forums and responses form mentors.

автор: Samuel

Feb 19, 2018

The course is not for people with not mathematical backgrounds plus its using matlab.. these days R and Python are more used in the industry for ML. I found to this course via friends that said it's hard but very recommended.. i think there are easier courses online that can deliver the same concepts

.

автор: Ivan Č

Feb 24, 2016

Certificate is expensive!

автор: Rui L

Oct 01, 2018

I would not recommend taking this course any more. (2018)

This course is showing its age and lots of concepts simply doesn't apply any more, considering how fast this field is changing.

автор: Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

автор: Anand R

Nov 20, 2017

To set some context: I am a graduate (PhD) in Computer Engineering from the University of Texas at Austin with over 10 years of experience in both academia and industry. My goal in taking this course was to learn the basics of Machine Learning, and understand what the current excitement about ML and AI is all about. I dedicated 3-4 hours every week, over the last 12 weeks, towards learning this course — and watched all the videos, reviewed all the lecture .pdfs and completed all the project assignments and all quizzes in the course on time.

About the course: This is one of the best courses I have taken (and I have taken more than 10 courses on coursera, edX and Udacity). Dr. Andrew Ng needs no certificate of approval from anyone. He is clearly a wonderful teacher, and I felt I struck a chord with him. There are few people who can explain complex concepts clearly without over-simplifying. Some people don’t have the ability, and often those who do, don’t care enough. The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down. Dr. Ng does that very well in several places and my deepest respects to him for doing that.

Clearly, Dr. Ng is a pactitioner in the field. The material was very well structured, very well paced and presented in bite-sized modules. The project assignments were both challenging and quite realistic. I feel a tremendous sense of confidence having completed this course, and I hope to try out some ML challenges on the web in the near future.

Last, but not the least, I cannot appreciate Dr. Ng more for the effort and dedication he has put into the subject and into his teaching. I felt a touch of nostalgia as the course ended suddenly with the last video (which was very moving, btw) and there was no NEXT button to click on. Being an educator myself, I know it takes a LOT of time and effort in developing a course. After completing this course, I felt I owed it to Dr. Ng. to purchase the course. I feel proud and happy to be certified as his student.

Thank you, Dr. Ng.

Thank you coursera.

автор: Irfan S

Apr 06, 2020

Extraordinary course for beginners (as well as for people with experience)!

If you are a beginner (as was I before taking this course), then this course is the perfect way to start learning Machine Learning. Even if you have some experience with ML, it'd be useful to learn about the recommended practices for choosing the right approach for a problem or something like debugging an algorithm.

Dr. Ng presents a huge amount of information in a structured manner, bundled with questions within videos that keep you focused. The quizzes and programming assignments complement the lecture videos. The programming assignments are in Octave. This is not necessarily a negative point (as other reviews are saying). If you are familiar with Python (or C/C++/Java etc), then it won't take you more than a few days at maximum to grasp the syntax of Octave. There is a lot of helper code in the programming assignments, so you mostly focus on the actual implementation of algorithms and such. Dealing with vectors and matrices in Octave has been a relatively better experience for me as compared to in Python. If you're stuck with programming exercises, then there are elaborate tutorials in the Resources section.

Possibly what I loved the most about this course is how Dr. Ng always mentions the recommended way of doing things (and how things are done in the industry). He also teaches you real life examples of how ML is currently being used by companies (for e.g. the course weeks on Recommender systems, Photo OCR, etc). So, if you're trying to learn ML for job prospects, this will be of great help.

Even though there's a fair bit of math (Linear algebra and some Statistics), Dr. Ng will help you walk through it and make you understand what you need to know.

Overall, this course has been a great help for a beginner like me. I recommend this to anyone who is looking for a course to start learning ML.

To Dr. Ng, the mentors of this course, and all the people who made this course possible, I want to thank you from the bottom of my heart. It's not easy creating so many hours of content (lecture videos, quizzes, assignments) and providing it online to thousands of people. I'm grateful for all your efforts.

автор: Kevin M

Dec 14, 2019

This is a terrific class! The Course is well structured in terms of videos, invideo pop-up quizzes, course notes, programming exercises, and the discussion boards & mentor community. The 11 weeks includes 8 programming exercises, with usually 5-6 "code submittals" per exercise.

The option of OCTAVE or MATLAB is great (I used MATLAB). A key aspect of this course is using vectorized methods in every programming assignment. There was always an option to write a procedure approach (e.g. do loop for summation steps like sum of squared differences for gradient descent or linear regression). The computational advantage, the simplicity of using vectors, and ending with "crisp" code is a great step

I have completed a similar class from MIT (Python or R based) and the exercises in this class were far superior in reinforcing the course materials.

This journey takes you through Supervised Learning models leveraging Linear Regression, Logistic Regression, Neural Networks, and Single Vector Machines and how gradient descent is the cornerstone to determine the theta values needed to optimize your hypothesis. Unsupervised Learning using K-means, PCA, and Anomaly Detection. Specific real life example for Recommender Systems, Character Recognition and large scale machine learning.

The various topics on "advice" by Professor Andrew Ng is invaluable. Understanding how to measure performance of your algorithm is key. Underfitting (bias) and over fitting (variance), regularization, learning curves, evaluation (precision, recall, and F1), and error analysis. Of particular note, is his understanding how to objectively determine how to what to work on next and how to apply "ceiling analysis" in complex pipeline ML applications.

A final note, the course mentors are unbelievable! Tom Mosher and Neil Osgrove are truly special. Their understanding of the material, their patience, and their incredible responsiveness is highly beneficial to the learning experience. You have to do the work and figure it out, but the mentors are there to help you navigate the Machine Learning journey!

автор: Boquan Y

Mar 19, 2020

Really a great course. It covered a large variety of currently popular machine learning algorithms, along with strategies to do machine learning projects. Professor Andrew really goes deep into how to optimize a machine learning model to reduce bias and improve performance with a lot of techniques, not just simply implement a fancy machine learning algorithm. At first, I complained about programming assignments because it is done in Matlab, but after I went through some of them I really discovered that Matlab is a powerful tool used for a broad range of purposes. The course goes beyond just model.fit(x,y) and model.predict(x,y), because you'll learn the essence and mathematical proof of each ML algorithm to really comprehend how each algorithm work and how optimization work. You can still learn to build ML models in python even by yourself after this course.

However, there are still some problems I want to mention. First, for some algorithm in the second half of the class (e.g. SVM with Gaussian kernel, anomaly detection), professor Andrew didn't sufficiently mention how math works, just giving the conclusion of how we should implement. I understand that maybe it is because the mathematic proof is too complicate here or it is not necessary to know the mathematic for mastering this type of algorithm. But I still hope that I can have a deeper understanding of every model based on mathematics. Another thing is that programming assignments didn't teach us how to plot graphs. Our work is only limited to "backend" implementation, which is the completion of the algorithm using a mathematical approach. I still hope Professor can introduce how to plot different kinds of graphs to really integrate our knowledge on "backend" to "frontend" for further data analysis.

Again, this is a great course, and anyone who completes this course will gain a lot of insights on ML and will have a solid understanding for future ML studying. Thank you, Professor Andrew!

автор: Anuradha R

May 24, 2020

I knew nothing about Machine learning when I started this course. I am going to start a job where I have to verify hardware for machine learning and I wanted to understand the vocabulary of machine learning better before beginning this new job. I got that from this course and a lot more! I liked the balance of mathematics, modeling and hardware aspects of this course. A key aspect of this course that elevates it is how Andrew always emphasizes evaluating the model / algorithm with real number outputs and not just plug ahead at full speed.

Thank you Andrew for putting this course together and making it accessible to all. I know how difficult it is to take a complicated topic that you are very conversant with and explain it in a way that a person not very familiar with the field understands it. And Andrew nailed this aspect.

This was also the very first course I have taken on Coursera. I am now inspired to try many more courses. Using Coursera to learn new concepts from home, without the pressure of time, money and grading is an incredibly liberating idea for me.

Overall, my experience with this Course and Coursera for me has been a 12/10.

автор: John H

Aug 22, 2019

This have been a very good and comprehensive introduction to Machine Learning, IMHO. It have given me the all basic introduction to ML that I could have hoped for. (I'm a senior practitioner of many forms of mathematical modelling and programming, as a former Astrophysics Phd.)

In particular, Andrew Ng is an excellent and experienced lecturer, and it's something that shows in that the course have been tested on thousands of students and over long time, such that for example exercises work very well in every little detail. (Sometimes quizzes may seem a little picky having to get nearly every little question right - but it's for really getting the understanding solid, and you can always improve your grade.)

Therefore, this must be a very good choice as an ML introduction, provided that you're willing to put in the effort of a few weeks on full time. (Albeit 11 weeks is for 'normal' university study schedule, and the course can be completed much faster on full time.) It should also compare well in generality compared to other courses (like Googles Machine Learning Crash Course).

автор: Mark M

Aug 11, 2016

Professor Ng is a great teacher, his course is both challenging and satisfying. The exercises require you to take one step beyond the lecture -- not just parrot back the transcript -- you have to think about the implications of what you've just studied. Yet Ng's presentations are lucid and informative and that next step is obvious, once you think about it.

My greatest challenge is that, although I have been programming for decades, I've only dabbled in a functional language like Octave and my last math class dates back to the 70s. However, the math requirements are not onerous and I'm struggling through the Octave assignments with some success.

Although the course is 11 weeks there are more than 16 lectures as some weeks have two complete sets of lectures PLUS there are assignments every week that take a few hours to complete. So while there is a little more work in this course than in other Coursera offerings there is great value for the money and time spent.

If you're interested in Machine Learning this course is a great place to start.

автор: Ozgur U

Jan 06, 2020

This is the first course I ever took on Machine Learning. I have a good background in linear algebra. Therefore, Mathematical aspects of the course was not a big challenge for me. At the same time, Professor Ng explains the ideas behind each ML algorithm in an easily comprehensible manner. It is easy to follow his videos except the sound quality. I would strongly recommend that they improve sound quality.

The quizzes are not very challenging and easily doable if you understand the lectures.

The assignments are easier than I expected. The whole structure of the algorithm is given to you and some parts of the assignments simply require writing one or two lines of codes. I would recommend them adding a capstone project at the end of the lectures so we can apply what we learned.

Overall, if you are looking for a fundamental introduction to ML and posses a basic knowledge in college level linear algebra, I would strongly recommend this course to you.

автор: Vikrant K

Aug 30, 2019

It's so wonderful that it can't be explained by the words and at the same time i am very sad that Ng sir has left us . i just love Ng sir , He is so wonderful person and teacher that can't be explained by the words .It's quite bit a big dream but i am dreaming of some day in the future where i am working with Ng sir on some machine learning problem and he is guiding me as he is doing now . I just love the course and also the mentors Mr. Neil Ostrove and Mr. Tom he had helped us to complete this course and assignment and also solved my useless something baby problems more carefully and i will help other student as guided by Ng sir in completing this course smoothely . and that's all . at the last i want to tell I just fall in love with Ng sir and coursera and the team . i have a big dream of meeting that my favourite Ng sir on some day.

Thank you

автор: Luca W

Jan 19, 2017

Thank you Professor Ng for taking the time to produce such a phenomenal course. As mystifying as machine learning can appear to be, your well-paced and digestible teaching style gave me the opportunity to understand. With fantastic lectures, mid-video quizzes, end of topic quizzes, and programming assignments, you as a student are given all the resources you need to absorb the material.

These eleven weeks really gave me the perspective and knowledge I sought for. This is the first online course that I have taken and I am inspired and excited for the future of machine learning and e-learning. The final heartfelt video was a perfect conclusion and I wish to return the sentiment of gratitude and appreciation.

Thank you again, and rest assured that your teaching is having a profound impact on peoples lives across the world.

автор: Tobias T

Jun 05, 2019

I've tried DataCamp and recently take my first course in Coursera. The difference is huge and important if anyone wish to learn more about ML or DS. This course does not focus much on 'just coding' the answer. It aims to teach you the logic, basic maths behind ML algorithms.

The coding exercise is challenging and fun aswell. It doesn't give you any 'fill in the blanks', so basically, after each exercise, you properly have some good understanding about the logic. Using Matlab/Octive is much better than I expect. Not that it is easy to use/understand, but it let you understand the Math better. e.g. when to transpose, how to use look at dimension before writing any codes. These exercises are at a level which you can easily transcend your understanding and knowledge to whatever Python or R you are using. !

автор: Arpit J S

May 01, 2020

Mr. Andrew Ng has mastery on Machine Learning. His method of teching is precise and lucid, often engaging us to think more on untouched aspects of ML. This was my first course and first step (a baby step) on any platform to understand and learn ML . Lucky to have enrolled for this amazing course and I sincerely thank him for being instructor on this subject and also tons of thanks to mentors who clear doubts in discussion forums. It helped a lot. Lastly , I think this course has clearly set my path towards advanced studies in ML. Although, statistics and some of the terms did bounce off my head few times, I hope to revisit and work on them more in future. Thankyou Andrew Ng Sir ! I am your fan now !!! :)

автор: Vincent C

Sep 25, 2019

After finishing the course, I feel much more confident in pursuing more advanced machine learning. The course teaches everything intuitively and in detail but maybe it could use some improvement to achieve perfection. It would be better if the course could provide pointers to some of the topics beyond the scope of the course such as the derivation of the back propagation, svm, pca, etc. Because often times when you search for derivations they might not be very useful for your levels, if course could provide some good references as some lecture notes after the video would be great for the students to gain even more solid groundings of the things behind the hood

Super thanks and thumbs up

автор: Vamshi B

Jun 06, 2019

As a machine learning newbie, I can say this course is really helpful to get in depth intuition on how machine learning algorithms work. Techniques to evaluate and improve our algorithms are also explained very well. Programming exercises are really challenging. Review questions are also crafted well. Though this course uses Octave/Matlab instead of python for programming, I find it quite useful to understand and implement algorithms easily. Only negative of this course is, mathematics involved is not explained in detail. Overall, this course has helped me a lot to understand machine learning in a better and useful way.