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

4.9
121,373 notes
29,802 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

MN

Oct 31, 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

PM

Jul 14, 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.

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176 - 200 sur 28,909 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 Subham B

Aug 30, 2019

This course is definitely not for beginners.

par Andy M

Sep 08, 2018

Huge amounts of assumed understanding make this course impenetrable.

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 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 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 이정은

Dec 10, 2018

Best lecture very nice

par Mahmut U

Dec 11, 2018

Excellent class with excellent teacher. I thank Andrew Ng for this amazing class.

par ankesh k

Dec 12, 2018

Amazing way of teaching by Sir Andrew Ng

par Shashikiran B L

Dec 10, 2018

Great In-depth course

par Zhanluo Z

Dec 11, 2018

Very structured, very useful and easy to follow.

par Manjunath G

Dec 12, 2018

Best place to start Machine Learning for Beginners for easy and effective understanding

par ayush

Dec 12, 2018

a great start for begineers in the field of machine learning

par Timothy K K

Dec 10, 2018

Great insightful course to open your eyes on the vast field of machine learning and get you started on the core concepts of the field

par Fernando O A

Dec 11, 2018

Excellent course! Andrew knows how to teach a subject that is not very trivial.

He uses a language that requires an abstraction of mathematical concepts, but without requiring a deep knowledge of formulas and calculations. He also manages to demand the least possible advanced knowledge, but it takes a little more dedication in programming and reasoning, achieving the goal of being understood through the preparation of well-elaborated exercises.

The Octave programming language is very simple and allows the dedicated effort in the exercises to really focus on understanding the algorithms and not on learning a new language.

Congratulations to Andrew, Coursera and others involved in the preparation of this course. I recommend everyone who wants to understand the basic concepts and algorithms related to Machine Learning.

par Mike F

Dec 11, 2018

Very valuable, well put together, excellent programming exercises.

par Silvian T

Dec 11, 2018

Exceptional!

par Jaideep M

Dec 12, 2018

This course is an excellent introduction to machine learning and it helps you learn a lot of machine learning concepts. The programming exercises are very well designed and teach you to implement a lot of popular machine learning algorithms. This course is highly recommended for beginners and those who want to make a career in machine learning.

par Ankit

Dec 12, 2018

Very good (ML) course for the beginners. :)

par Jinxi L

Dec 10, 2018

good stuff

par Roy

Dec 10, 2018

Very useful. The best course I've ever had in Coursera

par Denis T

Dec 11, 2018

It is a good idea to show algorithms work on real objects. the examples are simple-understanding becomes absolute.

par Daen K

Dec 11, 2018

Only basics

par Rajeev A

Dec 12, 2018

Simply Awesome ! Very thorough contents and Andrew Ng explained the concepts very clearly.

par Malan L E

Dec 12, 2018

It was really helpful for me to understand basic concepts of machine learning in order to start my career in the respective area. Thanks a lot for making all the materials available for free. I am hoping to buy this course in order to obtain the certificate. Thank you again!!!