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

116,957 notes
28,701 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


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.


Oct 03, 2016

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

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27276 - 27300 sur 27,934 Examens pour Apprentissage automatique

par Rosemberg R V

Aug 09, 2017

Es un buen curso para iniciarse dentro del mundo del machine learning. Abre la mente a otra área de conocimiento que utilizamos todos los días y que no sabíamos como se hacía.


par Selvakumaresan

Apr 13, 2016

Very Good Course, Introduction of Concepts of ML with practical hand on training and TIps/advice to apply in practice


Feb 27, 2016

Great experience for my first mooc

par Haydée H

Mar 13, 2017

I like very much the course, I recommend it. I would have liked learned more MATLAB parallel to the course.

par Stefan A

Jan 04, 2018

Some stuff is a bit outdated, otherwise great course!

par Vijayram R

Mar 30, 2016

very good

par yash g

May 04, 2017

A good course but needs to be updated and also new material has to be added.

par Viet A T

Nov 13, 2017

I wish assignments are written in Python!

par Praveen

Nov 19, 2017

Concepts were concise and clear. One added improvement could be - The course materials/ assignments could also be in Python/ R also since these languages are hot in ML world.

par surabhi s

Jun 03, 2017

Great Lectures by prof Andrew Ng. Had a good experience learning ML Concepts and Algorithms.

par Aviv E

Feb 25, 2017

A bit too simple and the programming exercises are too written out, but I have to admit, after this course I am able to talk about these algorithms intelligently. Thank you very much kind sir.

par Roberto M

Oct 21, 2016

Great overview of most promizing ML techniques. Just enough math to be able to use the knowledge but not too much to make the course heavy. Stimulating. Oriented to people that want to be operative on ML rahter than understand

par Vyom B

May 22, 2017

Deserves 5 stars if it was in python.


Apr 06, 2017

An excellent overview of machine learning techniques, supplemented by programming exercises in Octave or Matlab to reinforce the concepts introduced in the lectures.

par Rahul T

Sep 10, 2017

Please change matlab assignment to python

par Eric C

Sep 04, 2017

I very much enjoyed the class; I feel like I learned a lot. Coming from a physics background though, I found it perhaps a bit too light on the math - though I understand that this is meant to be an introduction to students from a broad range of backgrounds. Also, I would have preferred to use Python rather than Octave just because it is a more popular language, but it did make matrix operations and vectorization of programs very simple. All in all, very good course!

par William H

Jul 21, 2017

Great course ! It should have had more lessons on unsupervised learning to be perfect.

par Utkarsh J

Nov 03, 2017

Nice place to start for beginners in Machine learning . Will defineitely help you to grab on the theoretical concepts needed in this field . But not much of programming is involved in this course rather the focus is on the subject matter and algorithms.:)

par Mandar B

Oct 08, 2017

Appreciated: Practical oriented approach, small size of videos, regular & to-the-point quizzes and clear presentation.

Complaints: Conceptually rare.

par Leonardo O

Mar 20, 2017

I believe the course could go a bit more in detail on the algorithms and the mathematics behind it, but overall it was a great course.

par A J

Sep 25, 2017

The idea behind the algorithms are clearly presented by Andrew Ng.

par Sarath C P

Apr 05, 2018

Assignments are really tough

par Avais S

Feb 04, 2018

This is one of the best course on coursera helps beginners to learn about machine learning


Nov 05, 2016

It is a very good one thought i would have liked to have an explanation of the answers of some of the two-variable funtions questions proposed in the calculus part. Thanks a lot

par Niraj S A

Jan 29, 2017

The course was really helpfull for understanding machine learning concepts and application.