Chevron Left
Retour à Apprentissage automatique

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

4.9
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

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.

VB

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

Filtrer par :

51 - 75 sur 27,879 Examens pour Apprentissage automatique

par Marius N

Oct 31, 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

par Quoc-Viet P

Jun 25, 2018

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

par Cesare C

Jun 20, 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

par Miguel Á A S

Jul 24, 2019

This course is one of the most valuable courses I have ever done. Thank you very much to the teacher and to all those who have made it possible! I will recommend it to all those who may be interested.

par Ganesh K A

May 16, 2019

If it was in python, then it would have got 5 star from me.

par Harshal M

Mar 25, 2019

If this course was in python or R it would have been easier to understand. Octave/MATLAB is not that widely used.

par Rui C

Dec 12, 2015

However good the material and lectures may be, the use of an outdated version of Octave (which is not Mac-friendly and exceedingly brittle, to the extent where the supplied code requires manual patching in Windows and Linux) is a complete turn-off and makes it nearly impossible to complete the assignments on time unless you're prepared to spend at least twice as much time debugging your setup as doing the actual assignments.

I'll come back when this is done with R or Python.

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 Bhanuprasad T

Jan 01, 2019

Loved it. Easy and Excellent Course

par Panu M

Jan 02, 2019

Very eye-opening for a person with a very little knowledge of the aspects and maths behind machine learning. The exercises were somewhat difficult since it's been 15 years since my last maths class and I really haven't been doing it since. So a lot of effort really needed, but once you've done it, it feels great!

par Luka B

Jan 30, 2019

Great course, only a bit updated. Would be wonderfu if there was an update (or additional week of two) for 2019!

par Raneen H H

Feb 04, 2019

Great course - the assignments were extremely helpful and Professor's Ng's explanations set the fundamentals and cover advanced topics in machine learning.

par Joydeep S

Nov 07, 2018

Excellent course. Anyone interested in Machine Learning should definitely take this course. Thanks Andrew for making this.

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 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 Miklós L

Jan 15, 2019

Amazing class, great lecturer. A really good introduction and overview of machine learning concepts. It often skips the detailed mathematical background, to make it accessible for a wider audience, but I still found that enough information was given that allowed me to work out these details on my own. A lot of effort was put into creating the programming assignments, they provide a great hands-on experience with machine learning algorithms.

par Eugene M

Jan 04, 2019

Very useful course!

par YuShih C

Jan 04, 2019

Great introductory course for Machine Learning using MATLAB/Octave. Highly recommended.

par THIERRY L

Jan 04, 2019

Excellent

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 Roei B

Jan 18, 2019

10/10. Andrew is an amazing teacher. Thanks!

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 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 Zheng Y

Feb 23, 2019

The course is very well structured for me, a student who has some understanding of machine learning but would like to get a systematic introduction of the subject.

The course strikes a balance between depth and breadth. The amount of math and equations are just right. Prof. Ng did a good job stimulating the students' curiosity to dive deeper. And for those who want to get practical and hands-on, this course contains enough tools for machine learning practitioners.

I would recommend this course to anyone who is interested in machine learning but do not know where to start.

par Anish K A

Feb 22, 2019

Excellent course. I am not an expert in mathematics, but this course gives me a very good understanding of ML and algorithms.