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Avis et commentaires pour l'étudiant pour Apprentissage automatique par Université de Stanford

4.9
119,867 notes
29,424 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

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.

AA

Nov 11, 2017

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

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27926 - 27950 sur 28,537 Examens pour Apprentissage automatique

par Saiavinash B

May 01, 2019

can be improved in a detailed fashion

par Kelvin Y

Apr 17, 2019

Pretty good. Definitely not an expert yet, but it was very useful.

par Amar D

Apr 19, 2019

Informative course

par Ayaz K

Apr 17, 2019

Its an amazing course for beginners, but it would much better to be in python rather than octave.It helped me a lot in understanding the concepts of Machine Learning.

par Rajpal s D

Apr 19, 2019

This course is really good and a step towards the real world of machine learning and it's applications,

Thanks to coursera for such course.

par Aditya S

May 03, 2019

This is the best course for beginners. It teaches all the topics of machine learning with best quality quizzes. The only reason I am giving four stars is because it uses octave language instead of python.

par jay t

May 02, 2019

Great teaching!

par Alberto R A

May 04, 2019

very good class, but a little bit outdated.

par Lalit C

Apr 22, 2019

This course is really worth doing

par Aditya J

May 06, 2019

Great Course

par Jin-Woo K

May 07, 2019

It is a great course for the beginners of ML. It has a broad scope like any introduction class, so I think it became a good seed to self-study for me.

par Shashank G

Apr 24, 2019

Excellent Course, Thank you so much for this!!

par Chambrin

Apr 25, 2019

Le cours est très bien expliqué, les exemples sont clairs, et la difficulté croissante. Je ne mets pas 5 étoiles à cause de la qualité d'enregistrement du son qui est mauvaise.

par Vedrana S C

Apr 24, 2019

A very interesting class, I feel I have learned a lot. At moments I wished for a bit higher pace in the lectures.

par Alicja K

Apr 13, 2019

Nice course, but I have few comments: firstly, some statistical problems, although complex, might have been explained more clearly, which I have seen in the other courses. Secondly, I would expect some final hands-on, self driven project. I am not really sure whether I will be able to apply any of my data to the algorithms I have learnt.

par Prashant G

Apr 13, 2019

Did not rate octave, python would be a lot more practical as the deep learning courses are also in python. Extra admin for not much benefit

par Kairav P

Apr 13, 2019

it is a very good course for the start of machine learning.

par 陈市

Apr 27, 2019

部分阅读材料无中文,理解有难度

par Kalyani O

Apr 01, 2019

ourse content is really good. There seems slight dip in audio.

par RajaRajalakshmi

Apr 01, 2019

This was an exceptional class, got to learn lot of things and the videos of how to debug and what to do next was great tip for learners.

Thank you Andrew N G for making such a wonderful video !!!

par Rahul s

Apr 14, 2019

All course is good, the only thing is bad audio which cause some time to not understand the topic correctly.

par LILLITH T C

Apr 01, 2019

Well constructed course. Excellent material with regard to the various types of algorithms and how they are derived. Utilizing Octave was awkward and a bit clunky. I would have rather done the programmatic tasks in Python or a related language, but this was not a deal breaker. Some content could be updated to reflect current advances, but all around a very excellent course.

par 刘楠

Apr 02, 2019

课程不太深入,但是作为机器学习初学者入门的材料,和非机器学习专业的人来了解,还是不错的。

par AMIT D

May 11, 2019

Andrew Ng, has designed this course very well. Has spent right time on the challenging topics which helps to grasp them in most efficient manner!