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

4.9
étoiles
161,934 évaluations
41,533 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

AD
21 avr. 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

EJ
26 mars 2018

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

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26 - 50 sur 10,000 Avis pour Apprentissage automatique

par anand

11 nov. 2017

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

par Anup D

21 avr. 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

par Eric J

27 mars 2018

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

par Natasha

15 oct. 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.

par Markus S

25 janv. 2020

Perfect foundational overview of the topic with challenging exercises, at least for someone who left university over 20 years ago and has since then not done much with his skills in Linear Algebra ;-)

par Prakash M

14 juil. 2019

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

par Deleted A

2 avr. 2019

Have to give a star so I will give it one. Others rate this course highly. I don't know why.

Course states no requirement for knowledge of linear algebra. However this is not really practical and seems disingenuous. I have spent a lot of time re-learning linear algebra.

I have spent much more time on the work than the course states and unless you are currently involved in similar work you probably will too.

I have never received any response to the feedback I provided.

Many times I have been frustrated because the math material is treated casually but then later success on quizzes and assignments are based how well you understand the math. So while the instructor and content can treat the math as casually as they wish, unfortunately, you cannot be so casual.

par Rui C

12 déc. 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 Cesare C

20 juin 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 Carlos E R d S

16 juil. 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).

Andrew Ng is excellent

par Goulven G

10 janv. 2019

This course could be a nice introduction and overview of the Machine Learning field.

However, the video transcripts are TERRIBLE — do not attempt to find any traces of grammar in them ! After a while I figured there were lecture notes (seriously, why hiding them under Resources ??? some people don't want to or simply can't watch the videos), but some of them lack information needed for the quiz so for some sessions you still have to watch the videos or endure the transcripts anyway.

But MOST OF ALL, the course has an incredible number of (acknowledged) errors, sometimes critical for the programming assignments, and you have to dig into the forum and Resources Erratas to figure them. Given that this lecture has been online at least since 4 years and some people actually PAY FOR IT, I find this utterly disrespectful, hence my low rating.

Furthermore, note that the validation script for ex5 is too permissive : it accepts wrong linearRegCostFunction implementations, which makes the second part of the assignment quite painful to debug…

par Mathew L

25 sept. 2015

This course is absolute garbage. You get no feedback on your quizzes or assignments and the professor is one of the most boring I've ever seen. It's absurdly frustrating to repeatedly fail without any feedback as to why you're failing.

The lectures are clearly from a math perspective, as the prof simply draws what he's talking about on the slides. His hand writing is poor, and he does a lackluster job of explaining what exactly he's doing.

Finally, pure lecture with no notes is almost impossible to learn, as there's nothing to read and study.

I'd rate this course a 1/10, take the course on iTunes from Caltech instead.

par Rafael L d C

19 juil. 2019

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!

par Quoc-Viet P

25 juin 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 Abdul Q A

3 mars 2018

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

par Shweta K

26 oct. 2017

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

par Harshal M

25 mars 2019

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

par Karl M

11 août 2017

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

par OMKAR K D

30 mars 2019

Explained well but I was not able to implement it on exercise.

So I switched to other webites .Now I am back here and realized the value of this course

par Sergey K

24 janv. 2016

Level of difficulty of lectures is not correspond with level of quizzes. In lectures they are talking about simple stuff and then in quizzes they ask you about details they didn't mentioned. You could deducts this information though. But this is exactly the main problem with this course - for quizzes you should deduct and learn by yourself so much stuff, that videos start to be not worth your time.

par Rafael d S P

10 juin 2020

This is a great way to get an introduction to the main machine learning models. The professor is very didactic and the material is good too. I recommend it to everyone beginning to learn this science.

par Sunesh P R S

17 mai 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.

par Ziwei L

7 déc. 2015

The course is well organised, with cutting edge knowledge ready to use in our information era. And Andrew was really decent with clear illustration and explanations. I really enjoy taking this course!

par Mehdi A

24 févr. 2018

Too many trainings and assignments without enough practice, exercise and examples. This can be very confusing for a person taking the course for the first time.

par Jimmy C

18 mai 2019

I‘m a Chinese post-graduate student of Computer Sciense. This class is very useful to me because of it's amazing course videos and the well-designed programming exercises. It is really lucky to have this opportunity to find the course and to finish it. This class will be a footstone for further studying in AI field for anyone who just get started.