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

4.9
119,744 notes
29,390 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

RD

Mar 31, 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

RC

Jul 19, 2019

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!

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101 - 125 sur 28,505 Examens pour Apprentissage automatique

par Hacker O

Jun 17, 2019

very good!!

par Mohammed R

Aug 07, 2015

the audio is sometimes noisy, but everything else is perfect, thanks a lot

par Joy F Y

Aug 07, 2015

It's very useful

par Saiful I A

Aug 07, 2015

Very Nice

par Weixiang Z

Apr 03, 2018

Very nice course,. Give a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.

par Yashendra M

Jul 24, 2019

A great course to opt for. Learned many new things. All of them were relatable to the daily life of internet. A well made course. Thanks Andrew Ng for this course. You are a great teacher. Loved the coding in this. All those algorithms were awesome.

par runner_yang

Jul 25, 2019

Thank you sincerely! I have learned a lot through this course. I love Ng and coursera!

par Francisco M M

Feb 24, 2017

Excelente curso! No solo explica detalladamente el sustento teórico de los diversos temas, además propone ejemplos aplicativos sencillos que ayudan a una mayor comprensión. Y por si fuera poco toca temas algo complejos pero sin perder la excelente pedagogía.

Lo recomiendo!

par Tushar T

Aug 07, 2015

Its amazing course, very detailed and good explanation of each algorithm. Mr Andrew NG has good teaching skills, I am glad that I came across this course. Thanks Cousera. :)

par Cosmin V N

Aug 07, 2015

Amazing course. Complex topics explained in a way that anyone with a rudimentary understanding of math can follow.

par Brian

Aug 07, 2015

It's amazing, I can learn fanstastic stuff through this free course. There is no boundary. I could implement the machine learning code , and understand well. Thank you so much.

par Ajay T

Jul 29, 2019

Excellent course. Discussion forum help from the mentors was super in the first half of the course but towards the end the mentors did not participate

par Jaspinder S V

Aug 08, 2015

Awesome course for beginners.

par Pavel K

Jun 06, 2019

A great course.

par 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.

par Liesbeth v O

Jun 06, 2019

If you want to learn how to apply machine learning in a wide range of practical settings, this is the course to take! Professor Andrew Ng obviously put a lot of time and care into developing this course and preparing the excercises to provide you with a really smooth learning curve.

par Nathan M

Jun 05, 2019

I thoroughly enjoyed the videos and programming exercises. I think Dr. Ng has great insights that will help me approach future ML problems with greater understanding and efficiency.

par Jorge L R C

Jun 05, 2019

Even being for a "old" course, it has the very best ground of concepts and techniques of Machine Learning. I am very much satisfied and have learned a lot.

par Nimish B

Jun 05, 2019

I loved this course. Helped me learn about concepts specific to Machine Learning in a very interactive and intuitive manner. Working on Octave took time at first but is easy to pick up. Thank you Andrew Ng for this really well thought out curriculum!

par Hamed B

Jun 05, 2019

THE BEST COURSE IN ML BY FARRRRRRRR

par Vaibhav J

Jun 05, 2019

The explanation of each and every topic is so simple and easy. The course is taught by prof. Andrew Ang and covers the major concepts of machine learning. He also provides a good intuition about the topic so to understand them better. Overall this course is awesome and I would highly recommend to someone who is a beginner in Machine Learning. I am very grateful to Professor, Mentors and the Coursera for this amazing journey of 11 weeks in machine learning.

par 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. !

par ylfgd

Jun 06, 2019

very good

par Sohan J S

Jun 06, 2019

It was an amazing experience in learning Machine learning. I learnt a lot from this course. I thank the instructor, Prof. Andrew.

par Anith S

Jun 06, 2019

This is the first ever course I have taken on Machine Learning and I have to say that it was the best course that I have ever taken till I have taken the DeepLearinig Specialization by Andrew Ng.

I would highly recommend this course for anyone who wants to break into Machine Learning. Because it starts with the very basics and builds on it.

It currently may be bit outdated considering that it is thought using Matlab and not Python but it is excellent in explaining the core concepts and the algorithms of Machine Learning.

It is still a good course for breaking into Machine Learning.