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

4.9
121,268 notes
29,773 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

HS

Mar 03, 2018

My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.

ML

Aug 19, 2017

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

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28476 - 28500 sur 28,892 Examens pour Apprentissage automatique

par VIGNESHKUMAR R

Aug 23, 2019

good but very long course

par Tobbe R

Aug 21, 2019

Great content. For me that was a newbie on Octave and had not used Linear Algebra for 20 years it was hard to do the assignments.

par Giovanni S

Aug 21, 2019

Good!

par Jianguang G

Aug 22, 2019

Hope the code is written in python

par Lucien M G

Aug 20, 2019

The best course on machine learning that you can find.

par Niraj S

Aug 21, 2019

It would have been more beneficial if programming assignments were in python.

par Ritik S

Aug 21, 2019

Good

par Abdelrahman

Sep 13, 2019

Its very good course to start with, but the only disadvantage of this course is that it uses Matlab not Python

par Prasad N R

Sep 15, 2019

I am still going through the course. It is amazing. Most of the basics are relevant to ML. (It many not have deeplearning like GANs and some other shallow learning methods) But, that does not make this course any less relevant. It offers a solid introduction to matrix based processing (something I had been thinking about since a quite long time given that I have a GPU and taking a course in ML).

Octave is great too. Coupling Octave with Andrew Ng's teaching has been great! I have seen people commenting that Python is better. I have a Python background too. Programming language really does not matter to learn the nuts and bolts of ML (unless the language itself has some restrictions like Garbage collector in Android).

One improvement point (why I rated 4 out of 5 and not 5 on 5): Estimates of programming assignments are not correct. It requires reading through the material and sometimes, even though the required accuracy is obtained (without hardcoding anything), the solutions are not accepted. Usually it is due to bias not being considered properly. However, this is a major hurdle as the concepts will be clear and yet, due to this, time just goes off and assignments require significant time commitment (not 3 hours). Especially, for people without strong math background, these assignments may suddenly become difficult.

par Ankit S

Sep 15, 2019

This course is so well designed that even new users to Machine Learning find it easy to learn the concepts and follow the course smoothly. I really enjoyed the whole course but I also want that this course should include one more week on neural networks.

par Nathan D

Sep 14, 2019

If only it used Python, not Octave & Matlab.

par José V S d S

Aug 27, 2019

This course is very good and intuitive, I came here with just a little knowledge about AI and now I'm able to make my own system, applying it to things that we're not dealed in this course with excellence. I think that it is not 5 stars because of the errors in the lectures and proggraming exercises, but if I were only judging the knowledgement that Ng share, I'd give 5 stars.

par Rukmini M

Aug 26, 2019

This course is very good for beginners. It gives the intuition of various machine learning concepts. It can be understood easily without any prerequisites. There were notes for some videos. Similar notes can be provided for the remaining important concepts at the end of the course. This course would have been even more effective if it included a mathematical background for the concepts explained. Although it's included, a few topics lacked mathematical explanation.

par Daniel S H

Aug 27, 2019

Nice course, there was a lot of new staff that I didn't know, so I can say I ended the course with many many more knowledge than expected. I just would had like to have more of the final week content. Thanks

par ALFONSO F

Sep 17, 2019

普及有余,拔高不足

par Reaz H

Sep 18, 2019

The course is great. I learned a lot from it and Andrew is a very effective instructor. However, my only gripe is that the video and audio quality is not at par with the content and takes away somewhat from the experience. But this is a very good way to get into machine learning anyhow.

par Sachin G

Aug 28, 2019

Its really going to boost my skills. Thank u Coursera

par J H v d M

Aug 28, 2019

Excellent course; although I had done some classroom courses earlier also including advanced subject matter (deep learning, etc.) and in Python, this provides a fairly comprehensive overview and insights.

Although I initially found using a different language than Python somewhat redundant, it actually helped a lot in getting much better insight; it was having to do the assignments that helped the most getting it all down cold.

My suggestions: As I had a lot of help from the Octave reference manual (available online), add some links to relevant chapters and sections of that manual, as well as other reading materials; I managed to get most if not all done in vectorized form

Another suggestion is to include the concept of the pipeline at the very beginning of the course, and interweave it into the programming assignments. This not only makes reading the instructions a lot faster and clearer, it also teaches this essential skill; too important for it to be included in the last lectures

Some of the lectures were a bit long winded, but this is a personal impression; it is likely very helpful for others

Thanks a lot and now I'm continuing on some of the other courses / specializations, for which this course provides a valuable foundation

par Sidharth S

Aug 30, 2019

I would really like it to be implemented in Python. But this is easier for concept understanding. Though I need to do my project in Python usually.

par Anas A

Aug 30, 2019

GOOD

par Felix G

Aug 28, 2019

I really liked the way the algorithms where introduced and the on hand examples. The octave programming excercises were a little too easy though.

par Eskild E

Aug 28, 2019

Great course! I thoroughly enjoyed the material and the programming assignments (though I which they were in Python or R).

par Romik A

Sep 19, 2019

If you don't know this, don't worry!

par FarmerLi

Sep 18, 2019

Good for new starters.

par Mia W

Sep 18, 2019

Well taught, thorough course! Though some of the videos can be a bit slow paced, it's a very good course to invest your time in.