Chevron Left
Retour à Apprentissage automatique

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

4.9
121,183 notes
29,752 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)

RC

Jul 19, 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!

Filtrer par :

176 - 200 sur 28,880 Examens pour Apprentissage automatique

par Ross K

Oct 10, 2015

The course is more an exercise in flexing Ivy vernacular than it is actually teaching. The learning curve is too steep to be useful to the majority of potential registrants. You're interested in this course either to (a) learn something about an exciting and ever changing field and/or (b) to have the Stanford logo on your LinkedIn profile. In both cases, move on. The curve is far too steep to be useful or to merit the countless additional hours of background learning the course should have done to bridge the gap.

par Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

par Subham B

Aug 30, 2019

This course is definitely not for beginners.

par omri g

Nov 11, 2015

Been asked to re-take all assignments *after* paying for a certificate! I wil never pay for a Coursera course again, and I would not recommend my friends to do so

par Bayram K

Feb 17, 2017

I would rename this course as Programming Octave with Application to Machine Learning rather that Machine Learning. Once you start the course you will have to focus on Octave rather than on ML topics if you want to do programming exercises. There is no degree of freedom in programming. You are provided with a lot of weird Octave codes which you will have to complete instead of writing yourself from scratch. More than 50% of my time was spent in order to learn Octave and understand (guess!!!!) Octave codes.

So, if you really want to learn ML and try it in practice this course is not for you. However, you could just watch the videos whose level is not more that elementary introduction to ML.

par David C

Apr 02, 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 Govinda R

Nov 17, 2018

Very well structured with real life applications. Really helpful in understanding the concepts, modelling of machine learning problems & applications

par 赵俊文

Nov 18, 2018

GG

par XIAOYU B

Nov 19, 2018

非常棒的Machine Learning入门课程

par Michal S

Nov 19, 2018

Great course!

par Zejin H

Nov 18, 2018

Thank you Professor Andrew NG for this interesting introduction to Machine Learning World.

Coursera is such a great platform for people around the world to get the knowledge as they wish.

Thank you again for all the contribution done by your team.

par Amit R

Nov 18, 2018

Really enjoyed this course. Although being my first computer science course I still found it very easy to follow and understand. Learned some very important topics and will definitely try to finish the rest of the courses in this series.

par Pedro H

Nov 19, 2018

This was an amazing course to get started with machine learning. The pace and motivational support from Andrew was great. The content had the right level of difficulty, not too hard, not too easy. This the best overview of the techniques used for machine learning and applications with the right level of detail. The errors in lectures are all listed in the many erratas.

par Srikanth N

Nov 18, 2018

Great course! Be patient and solve all problems. Well worth it.

par Dipesh P

Nov 18, 2018

Both Prof Ng and Tom are excellent educators. I strongly recommend this course to anyone who is looking to get into this field. Also, given the quality of the education and the cost, it's a great value!!

par Andrii K

Nov 19, 2018

The best online course to get started with machine learning. Thank Andrew Ng for a such amazing experience.

par EL M A

Nov 19, 2018

The course was largely helpful over many sides. I've learned some of new concepts about machine learning and how it's applied on a couple of problems. One of the greatest thing in the course is the way Mr. Ng explains easily the concepts. Thank you !

par liyong

Nov 19, 2018

very nice course. The concepts are clarified very well.

par Reece E

Nov 19, 2018

Excellent introductory course to machine learning, with good examples and quizzes to help consolidate the training material.

par Nikita G

Nov 18, 2018

Awesome. Just awesome.

par Umesh K L

Nov 19, 2018

Excellent Course.

par Rahul C

Nov 19, 2018

Sooo good!!

par sol87

Nov 17, 2018

super awesome lessons

par Ethan ( W

Nov 17, 2018

Best course for machine learning

par Kostas P

Nov 18, 2018

An excellent course, fine structured, well organized, very dense content suitable for working people.

I fully recement it.