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

4.9
116,739 notes
28,641 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

FG

Jul 21, 2019

Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.

MS

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.

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76 - 100 sur 27,791 Examens pour Apprentissage automatique

par 个a

Apr 02, 2019

excellent class.worth your time and thank you ng

par Mohammed M

Apr 03, 2019

I learned a lot from this course, very recommended

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 Asmita P

Apr 08, 2019

Hi Andrew,

I liked this machine learning course so much. I enjoyed doing all the assignments. I am working in IT industry. I was thinking of working in a new age technology and then my brother told me about this course as I love maths and programming.

I am looking forward to get deep knowledge in machine learning.

Thank you,

Asmita Patil

par Tushar A

Apr 10, 2019

Very easily explained all the complex topics.

par Vivek R

Mar 12, 2019

This course is very well designed, covers a lot of topics with a lot of rigourous detail, but Andrew Ng introduces them giving some intuition about them, before diving into the deeper Maths. Assignments are very challenging, but with some boilerplate code already done, they are immensely satisfying, as you end up achieving with some implementations of pretty cool problems. I have done linear algebra and regression and PCA before, so was able to complete it rather quickly, but this should be very approachable and useful for everyone.

par DEEPANJYOTI S

Mar 11, 2019

This is a very good course which gives a good solid foundation in the basics concepts of Machine Learning. Prof. Andrew explains reasonably complicated algorithms in a very intuitive way which goes reasonably deep, but at the same time doesn't overwhelm the student with a lot of underlying mathematics. The course structure also follows a very natural progression (linear regression --> logistic regression --> neural network --> SVM) and bringing in other basic concepts like feature normalization, regularization, measurements etc. along the way. Definitely one of the better designed courses I've seen so far.

par Subham

Mar 03, 2019

The real way to learn Machine Learning is this, no black box;understanding using pure mathematics makes it more interesting, and as I was solving the programming exercises I got to know, how simply vectors and calculus can be used to represent complex mathematical formulas. All the hours completing this course was worth. Once I started using machine learning libraries, all concepts were no longer black box for me, suddenly everything started making sense. Highly Recommended course for beginners.

par Jiyuan Z

Mar 03, 2019

Great!

par Jasurbek N

Mar 17, 2019

Great for beginning into Machine Learning.

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 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 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 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 Yang M

Aug 14, 2018

Majored in actuarial science so machine learning is not a new word to me. Some of the techniques are very simple such as linear regression. Andrew is so clear and organized which helps me figure out a line in between. The projects are pretty easy but sometimes need plenty of time to debug.

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 Joydeep S

Nov 07, 2018

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

par Zhang R

Aug 07, 2015

interesting, exciting

par Jose E

Apr 21, 2019

It was a great way to remember and learn meany things and get tips which I wouldn't have gotten by my self. Thanks a lot!1

par Martins R

Apr 24, 2019

This was the hardest thing I've done in ages. I gave up at some point until a breakthrough in programming - I learning to use operations with matrices. I did all programming assignments in python. Couldn't finish the Neural Network - I was stuck for a month because I couldn't wrap my head around mathematical operation in backpropagation. Overall this was a journey. Every morning and evening learning on the way in the bus to and from work. Also lonely weekends. Finished. Can't thank you enough.

par Manolache

Apr 24, 2019

This was just great. Thank you to everyone who was involved in making this quality information available to us! Thank you, Andrew, you are a great teacher!

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