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

4.9
étoiles
136,417 évaluations
34,221 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

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.

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126 - 150 sur 10,000 Avis pour Apprentissage automatique

par Megil P

Mar 16, 2019

Tried different youtube tutorials before, but this course was the one, that gave me true understanding about machine learning and how it basically works. I'll always recommend this course to start a machine learning journey! Thanks to Andrew NG for this course and for his effort!

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 Jawsem A H

Jan 21, 2019

Andrew Ng teaching is great! He breaks complex concepts into easier to consume chunks. He not only explains how to implement specific machine learning algorithms, but how to recognize when there are issues with the models you create and how to address them.

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 Praveen K

Jan 18, 2019

This course is very well put together from beginning to the end. The simplicity and approach towards teaching this kind of new and complex subject is amazing. I highly recommend for anyone who would like to get into Machine Learning space.

par Shanthan K P

May 25, 2020

A very good course packed with the fundamentals of ML. It has given me a great overview of what ML is and the assignments were well organized. Neil Ostrove and Tom Mosher were very quick at replying to the queries and were very helpful!

par Tony X

Jun 05, 2019

Quite good, suggest for beginners. There is no much mathematics knowledge deeply involved.

Andrew Ng used a simply way to describe machine learning algorithm. It's really helpful to understand the concept.

Thank you very much!

par Amine M

Apr 11, 2019

I took this class to recap my ML knowledge. It filled up my ML knowledge gap! Anyone can take this class, regardless of background or level. There is always something new to learn in each lecture and topic!

par Omri M

Jan 16, 2019

terrific course, good balance of both high and ground level teaching. Good, hands-on experience. I actually appreciate the fact it isn't python or R - this way it's not geared toward a specific crowd

par Muhilraaj A R

Jan 02, 2020

Thoroughly enjoyed by doing this course.Gained lots of Knowledge on machine learning and practical skills on applying it.Thank you Andrew Ng sir,Standford University and Coursera for this course.

par Luís R

Jan 17, 2019

The course has an adequate degree of dificulty. It is not easy. But, the subject matter demands for that specific degree of detail if we really like to actually do something with machine learning.

par Sai G K

Jan 19, 2019

This is a great course for someone looking to learn Machine Learning from the ground up. I would suggest this course to everybody from beginners to professionals. Andrew Ng is an awesome teacher.

par Jag S S

Jun 09, 2019

Excellent course, everything is taught from the scratch. Anyone from any background can learn a lot about machine learning through variety exercises, tutorials and lectures. Highly recommended.

par Harshit S

Jun 06, 2019

Very Good Course to start into machine learning, It uses Matlab which is very useful, all mathematics behind different algorithm nicely explained by instructor, Instructor is very good teacher

par Mohamed M K

May 11, 2020

Really amazing course, Andrew Ng is one of the most successful professors in the world, not only he briantly teaches ML/AI, but he also does it with great sense of humility. Thank you Sir!!!

par Tu N

Nov 03, 2019

This is one of the best online course I have learned over many years. Thank you very much Prof. Andrew Ng. Highly recommended for whom want to learn about AI & Machine Learning subject.

par Gil B

Jun 06, 2019

The instructor gives simple explanations, yet covers all the topics deeply. The coding exercises are well designed and teach you haw to write machine learning with no past experience.

par Lakshya G

Jan 02, 2020

Really well defined course on Machine Learning. It would be ideal if you have some background knowledge on Math. Do Linear Algebra from Youtube ( Linear Regression) as a compulsion.

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 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 John M

Dec 13, 2018

Very detailed. The programming exercises were very well made, it was great to be able to implement the parts of the exercise one by one, and see the effect that each step has.

par Adam D H

Jan 02, 2020

This class is well worth your time to gain an excellent survey of the current ML state of the art processes. Very enjoyable and well taught, and the resources are very good.

par Rajkumar B

Aug 15, 2018

Much better than the lecture on machine learning than that is offered in my university. only problem is that if you are a fluent user of Matlab, you should skip week 1 and 2

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 Antoine G

Jan 02, 2020

A really good introduction to machine learning with real and practical examples. I can't believe we did so much in such a short time.

Well done professor Andrew Ng !