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Avis et commentaires pour d'étudiants pour Apprentissage automatique par Université de Stanford

4.9
étoiles
149,275 évaluations
38,030 avis

À propos du cours

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....

Meilleurs avis

RP

Jun 10, 2020

This is a great way to get an introduction to the main machine learning models. The professor is very didactic and the material is good too. I recommend it to everyone beginning to learn this science.

RS

Aug 13, 2019

Andrew Ng is a great teacher.\n\nHe inspired me to begin this new chapter in my life. I couldn't have done it without you\n\nand also He made me a better and more thoughtful person.\n\nThank You! Sir.

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76 - 100 sur 10,000 Avis pour Apprentissage automatique

par Rafael d S P

Jun 10, 2020

This is a great way to get an introduction to the main machine learning models. The professor is very didactic and the material is good too. I recommend it to everyone beginning to learn this science.

par Ziwei L

Dec 07, 2015

The course is well organised, with cutting edge knowledge ready to use in our information era. And Andrew was really decent with clear illustration and explanations. I really enjoy taking this course!

par abbas k

May 30, 2019

so useful

par Mirko J R

Apr 02, 2019

Excellent lessons by Prof. Andrew Ng.

However very poor support. No answers from any mentor along lessons, you should resolve all doubts by yourself.

I had a problem with my ID verification, I was waiting for a long time without any responses.

Also, it's difficult to contact persons who could support you, I tried to contact someone but just found a Bot. Terrible support.

par Hu L

Feb 14, 2018

Too easy and too slow

par Abdelhakim M

Jun 11, 2020

The course didn't convince me at all. Practice and applications in real life are in short supply. I missed the art and pedagogy of Trainer.

The certificate is a very poor certificate , no information about contents. No duration of the course is mentioned. It looks like a one day course certificate. This course is 11 Week long. Never again.

par Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

par Arunesh G

Apr 21, 2020

The BEST course I ever had in my life, even better than a typical classroom based interactive teaching.

This course has the best mix of perfect pace and accurate (to the point) material.

With ample examples, accurate content, greater student-teacher interactions (via programming assignments, quizzes, etc...), and THE BEST TEACHER "Professor Andrew NG", this course is exceptionally the best course one can get in his/her life.

This course is best for beginners as well as intermediate learners.

In the video lectures, not even a sigle second is wasted on off-topic discussion. Each and every second is utilized to the fullest.

In this course, most derivations (complex ones) are skipped, but that is done to help us to focus on the core of machine learning rather than diverging somewhere else. Also, in the end Professor NG teaches about the ceiling analysis which is how and where to focus resources in the development of machine Learning Algorithm, which is not taught in most of the courses I have seen so far.

Overall, this is the best course one can get.

Thanks to Professor Andrew NG

par Emmanuel N

Dec 06, 2018

Amazing course. I had no idea of programming and my maths were more than rusted, but the way the lessons are taught, made the way a whole lot easier. If you're like me (zero programing and maths), it's no easy task to complete the course. But if you put the right amount of effort, patience and dedication, combined with the great videos and reference material, is totally doable.

par Nicholas D

May 14, 2019

Truly an exceptional class. Not often will someone with a deep proficiency in a discipline have the time or incentive to share their insights and teach to others; this class is a rare exception, and given the vital importance of machine learning to the future, I have a great appreciation and debt to Andrew Ng.

par Simin L

May 14, 2019

Great class! Should be recommended for every individual who wants to learn machine learning and don't have time or oppotunity to take a class at their own univerisity, this class is a guidance for the basis of machine learning and gives me instructions where to go next. Thank Ng really much.

par 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 ;).

par Saurabh C

Jul 11, 2020

One of the best online courses I have attended in a decade. Thank you to Coursera for making this course available. I cannot express my gratitude enough to professor Andrew Ng for this awesome course!

par Pardis J Z

Jun 30, 2020

I really enjoyed this course. I learned new exciting techniques. I think the major positive point of this course was its simple and understandable teaching method. Thanks a lot to professor Andrew Ng.

par claire.hou0701@gmail.com

May 18, 2019

sehr gut!

par Armen M

Apr 09, 2020

THIS IS A REVIEW FOR BEGINNERS

ADVANTAGES OF THE COURSE

When I remember myself deciding whether or not I should take the course, the questions that concerned me the most were these ones.

1. Since I am a beginner in this field, will the course work for me?

2. Did this course get outdated? (For those who don't know, the professor uses Octave)

3. In the end, will I feel like I can do some Machine Learning projects all by myself?

For those who have the same questions, here are the answers for you )

1. Yes, the course will work for you even if you are an absolute beginner like I was at the time (I did not know any linear algebra), It does get annoying sometimes and you feel a lot of pressure at some point of the course, but a hard-working person can surely get through it. Mentors are active and very helpful if you get stuck on something.

2. This question is a big NO for me, here is why: When you are learning something from the very bottom it is super important to learn the hard way, which is the same as the old way. When you come across an easier path, you understand and grasp it way better. For Octave, many tasks require multiple lines of code, whereas in Python it is just one line. You have to do it at least once with Octave to understand how it works in Python.

3. No, you would not probably be able to start a project on your own, you would need some additional source. But, the point is that you now have a general understanding of what machine learning is, what are important algorithms and what are the key points you should consider when doing project. This is the base that every person should have.

DRAWBACKS OF THE COURSE

Although I loved the course, I could not give it 5 stars because it would have been unrealistic. The lectures of the course have an incredible amount of errors. You should be careful. Although all the errors are covered in the Errata section, it still was annoying to open the section every time when I started a new lecture. to check for errors I am about to see.

Another drawback was the programming assignments. They were not explained well and I almost always had to refer to extra Tutorials made by Mentors.

Special Thanks to Professor Ng and all the Mentors!

par Alexander C

Jul 16, 2020

This was a great course, and I highly recommend it! Andrew Ng made me feel like he's my machine learning pal. I can see why this course is so popular.

I docked it a star because the assignments could really use an update. The work flow for completing them includes consulting multiple documents of (sometimes contradictory) instructions as well as errata documents, tutorial posts, and discussion threads. It's too much and when your script isn't working it makes it difficult to know whether you made a mistake or if maybe there's some updated note that you missed. If all of the assignment notes were just consolidated into one document, then five stars for sure!

par Jerome T

Mar 06, 2019

I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.

par 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.

par 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.

par 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.

par 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

.

par Ivan Č

Feb 24, 2016

Certificate is expensive!

par Ястрембский А Н

Oct 01, 2020

В требованиях к прохождению курса необходимо указать "владение университетским курсом высшей математики" и "математический английский" - без него тут нечего делать, поскольку текстовка на русском языке не совпадает с тем, что говорит лектор ни по смыслу, ни, начиная со второй недели, по времени.

Никаких пояснений по алгоритмам или логике происходящего в курсе нет: вот формула, вот задание. Иди, решай. Курс аналогичен по составу самоучителям по рисованию: "Рисуем круг, рисуем круг побольше, дорисовываем сову."

par Germain M P

Dec 05, 2019

Poor audio and video quality, what compromises the learning process