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

4.9
étoiles
135,356 évaluations
33,877 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

CC

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)

NN

Oct 15, 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

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176 - 200 sur 10,000 Avis pour Apprentissage automatique

par Mohammed M

Apr 03, 2019

I learned a lot from this course, very recommended

par Yuhang T

Jan 17, 2020

thank you, I have learned a lot from this class.

par 个a

Apr 02, 2019

excellent class.worth your time and thank you ng

par Roei B

Jan 18, 2019

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

par zhaoyi

Jan 02, 2020

Very good intro course to machine learning

par Zilin L

Jun 07, 2019

几乎没有数学要求,老少咸宜。

编程作业设计非常用心,专注于让学生完成核心人物。

好评!

par Hamed B

Jun 05, 2019

THE BEST COURSE IN ML BY FARRRRRRRR

par Bhanuprasad T

Jan 01, 2019

Loved it. Easy and Excellent Course

par Jaspinder S V

Aug 08, 2015

Awesome course for beginners.

par Mulat Y C

Feb 14, 2020

Machine Learning

Data Science

par 梁驰

Feb 08, 2020

喜欢吴恩达教授的课,讲的非常的好!教授很谦虚!赞赞赞!

par chandan k

Jun 06, 2019

Great course to study!

par Eugene M

Jan 04, 2019

Very useful course!

par Joy F Y

Aug 07, 2015

It's very useful

par Pavel K

Jun 06, 2019

A great course.

par Hacker O

Jun 17, 2019

very good!!

par Stephen M

Jun 05, 2019

Very useful

par ylfgd

Jun 06, 2019

very good

par THIERRY L

Jan 04, 2019

Excellent

par Saiful I A

Aug 07, 2015

Very Nice

par Vivek K

Dec 13, 2018

Awsome

par Lichen N

Aug 28, 2019

深入浅出

par Armen M

Apr 09, 2020

THIS IS A REVIEW FOR BEGINNERS

ADVANTAGES OF THE COURSE

When I remember myself deciding whether or not I should take the course, the questions that concerned me the most were these ones.

1. Since I am a beginner in this field, will the course work for me?

2. Did this course get outdated? (For those who don't know, the professor uses Octave)

3. In the end, will I feel like I can do some Machine Learning projects all by myself?

For those who have the same questions, here are the answers for you )

1. Yes, the course will work for you even if you are an absolute beginner like I was at the time (I did not know any linear algebra), It does get annoying sometimes and you feel a lot of pressure at some point of the course, but a hard-working person can surely get through it. Mentors are active and very helpful if you get stuck on something.

2. This question is a big NO for me, here is why: When you are learning something from the very bottom it is super important to learn the hard way, which is the same as the old way. When you come across an easier path, you understand and grasp it way better. For Octave, many tasks require multiple lines of code, whereas in Python it is just one line. You have to do it at least once with Octave to understand how it works in Python.

3. No, you would not probably be able to start a project on your own, you would need some additional source. But, the point is that you now have a general understanding of what machine learning is, what are important algorithms and what are the key points you should consider when doing project. This is the base that every person should have.

DRAWBACKS OF THE COURSE

Although I loved the course, I could not give it 5 stars because it would have been unrealistic. The lectures of the course have an incredible amount of errors. You should be careful. Although all the errors are covered in the Errata section, it still was annoying to open the section every time when I started a new lecture. to check for errors I am about to see.

Another drawback was the programming assignments. They were not explained well and I almost always had to refer to extra Tutorials made by Mentors.

Special Thanks to Professor Ng and all the Mentors!

par Jerome T

Mar 06, 2019

I like the course very much. One point where it could be improved are the assignments: it is really nice to be guided and to have a big part of the programming prepared but the drawback is that many times I didn't feel in control of what was happening. For example, that was hard to know basic features of the implementation (is this data a row vector? a column vector?) since I didn't decide it. This leads me to spend quite some time on trying to fix simple problems. In short, I wish I had felt more "empowered" during the assignments.

par Saideep G

Apr 09, 2019

Very well made, well paced. Better than majority of college courses. Some errors do pop up midway through the course that should be addressed. It can be frustrating to push through these issues sometimes but they are the only thing keeping from 5 stars.