Retour à Apprentissage automatique

4.9

étoiles

129,805 évaluations

•

32,052 avis

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....

Jun 17, 2017

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too..

Apr 18, 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

Filtrer par :

par John H

•Aug 22, 2019

This have been a very good and comprehensive introduction to Machine Learning, IMHO. It have given me the all basic introduction to ML that I could have hoped for. (I'm a senior practitioner of many forms of mathematical modelling and programming, as a former Astrophysics Phd.)

In particular, Andrew Ng is an excellent and experienced lecturer, and it's something that shows in that the course have been tested on thousands of students and over long time, such that for example exercises work very well in every little detail. (Sometimes quizzes may seem a little picky having to get nearly every little question right - but it's for really getting the understanding solid, and you can always improve your grade.)

Therefore, this must be a very good choice as an ML introduction, provided that you're willing to put in the effort of a few weeks on full time. (Albeit 11 weeks is for 'normal' university study schedule, and the course can be completed much faster on full time.) It should also compare well in generality compared to other courses (like Googles Machine Learning Crash Course).

par Mark M

•Aug 11, 2016

Professor Ng is a great teacher, his course is both challenging and satisfying. The exercises require you to take one step beyond the lecture -- not just parrot back the transcript -- you have to think about the implications of what you've just studied. Yet Ng's presentations are lucid and informative and that next step is obvious, once you think about it.

My greatest challenge is that, although I have been programming for decades, I've only dabbled in a functional language like Octave and my last math class dates back to the 70s. However, the math requirements are not onerous and I'm struggling through the Octave assignments with some success.

Although the course is 11 weeks there are more than 16 lectures as some weeks have two complete sets of lectures PLUS there are assignments every week that take a few hours to complete. So while there is a little more work in this course than in other Coursera offerings there is great value for the money and time spent.

If you're interested in Machine Learning this course is a great place to start.

par Ozgur U

•Jan 06, 2020

This is the first course I ever took on Machine Learning. I have a good background in linear algebra. Therefore, Mathematical aspects of the course was not a big challenge for me. At the same time, Professor Ng explains the ideas behind each ML algorithm in an easily comprehensible manner. It is easy to follow his videos except the sound quality. I would strongly recommend that they improve sound quality.

The quizzes are not very challenging and easily doable if you understand the lectures.

The assignments are easier than I expected. The whole structure of the algorithm is given to you and some parts of the assignments simply require writing one or two lines of codes. I would recommend them adding a capstone project at the end of the lectures so we can apply what we learned.

Overall, if you are looking for a fundamental introduction to ML and posses a basic knowledge in college level linear algebra, I would strongly recommend this course to you.

par Vikrant K

•Aug 30, 2019

It's so wonderful that it can't be explained by the words and at the same time i am very sad that Ng sir has left us . i just love Ng sir , He is so wonderful person and teacher that can't be explained by the words .It's quite bit a big dream but i am dreaming of some day in the future where i am working with Ng sir on some machine learning problem and he is guiding me as he is doing now . I just love the course and also the mentors Mr. Neil Ostrove and Mr. Tom he had helped us to complete this course and assignment and also solved my useless something baby problems more carefully and i will help other student as guided by Ng sir in completing this course smoothely . and that's all . at the last i want to tell I just fall in love with Ng sir and coursera and the team . i have a big dream of meeting that my favourite Ng sir on some day.

Thank you

par Luca W

•Jan 19, 2017

Thank you Professor Ng for taking the time to produce such a phenomenal course. As mystifying as machine learning can appear to be, your well-paced and digestible teaching style gave me the opportunity to understand. With fantastic lectures, mid-video quizzes, end of topic quizzes, and programming assignments, you as a student are given all the resources you need to absorb the material.

These eleven weeks really gave me the perspective and knowledge I sought for. This is the first online course that I have taken and I am inspired and excited for the future of machine learning and e-learning. The final heartfelt video was a perfect conclusion and I wish to return the sentiment of gratitude and appreciation.

Thank you again, and rest assured that your teaching is having a profound impact on peoples lives across the world.

par Tobias T

•Jun 05, 2019

I've tried DataCamp and recently take my first course in Coursera. The difference is huge and important if anyone wish to learn more about ML or DS. This course does not focus much on 'just coding' the answer. It aims to teach you the logic, basic maths behind ML algorithms.

The coding exercise is challenging and fun aswell. It doesn't give you any 'fill in the blanks', so basically, after each exercise, you properly have some good understanding about the logic. Using Matlab/Octive is much better than I expect. Not that it is easy to use/understand, but it let you understand the Math better. e.g. when to transpose, how to use look at dimension before writing any codes. These exercises are at a level which you can easily transcend your understanding and knowledge to whatever Python or R you are using. !

par Vincent C

•Sep 25, 2019

After finishing the course, I feel much more confident in pursuing more advanced machine learning. The course teaches everything intuitively and in detail but maybe it could use some improvement to achieve perfection. It would be better if the course could provide pointers to some of the topics beyond the scope of the course such as the derivation of the back propagation, svm, pca, etc. Because often times when you search for derivations they might not be very useful for your levels, if course could provide some good references as some lecture notes after the video would be great for the students to gain even more solid groundings of the things behind the hood

Super thanks and thumbs up

par Vamshi B

•Jun 06, 2019

As a machine learning newbie, I can say this course is really helpful to get in depth intuition on how machine learning algorithms work. Techniques to evaluate and improve our algorithms are also explained very well. Programming exercises are really challenging. Review questions are also crafted well. Though this course uses Octave/Matlab instead of python for programming, I find it quite useful to understand and implement algorithms easily. Only negative of this course is, mathematics involved is not explained in detail. Overall, this course has helped me a lot to understand machine learning in a better and useful way.

par DEEPANJYOTI S

•Mar 11, 2019

This is a very good course which gives a good solid foundation in the basics concepts of Machine Learning. Prof. Andrew explains reasonably complicated algorithms in a very intuitive way which goes reasonably deep, but at the same time doesn't overwhelm the student with a lot of underlying mathematics. The course structure also follows a very natural progression (linear regression --> logistic regression --> neural network --> SVM) and bringing in other basic concepts like feature normalization, regularization, measurements etc. along the way. Definitely one of the better designed courses I've seen so far.

par Anith S

•Jun 06, 2019

This is the first ever course I have taken on Machine Learning and I have to say that it was the best course that I have ever taken till I have taken the DeepLearinig Specialization by Andrew Ng.

I would highly recommend this course for anyone who wants to break into Machine Learning. Because it starts with the very basics and builds on it.

It currently may be bit outdated considering that it is thought using Matlab and not Python but it is excellent in explaining the core concepts and the algorithms of Machine Learning.

It is still a good course for breaking into Machine Learning.

par Zheng Y

•Feb 23, 2019

The course is very well structured for me, a student who has some understanding of machine learning but would like to get a systematic introduction of the subject.

The course strikes a balance between depth and breadth. The amount of math and equations are just right. Prof. Ng did a good job stimulating the students' curiosity to dive deeper. And for those who want to get practical and hands-on, this course contains enough tools for machine learning practitioners.

I would recommend this course to anyone who is interested in machine learning but do not know where to start.

par Walter E P

•Dec 23, 2019

Great Course!. I took this course after having been formally trained in topics such as Numerical Optimization, Neural Networks, Genetic Algorithms, Linear Regression and other topics and I found these classes to be both very informative and refreshing. Learned something that sometimes some courses out there forget to mention which is how to draw meaningful statistics to analyze your algorithms performance and also things like what do work on next. I definitely advice people to take this course even if you are a pretty advanced learner on these topics.

par Vivek R

•Mar 12, 2019

This course is very well designed, covers a lot of topics with a lot of rigourous detail, but Andrew Ng introduces them giving some intuition about them, before diving into the deeper Maths. Assignments are very challenging, but with some boilerplate code already done, they are immensely satisfying, as you end up achieving with some implementations of pretty cool problems. I have done linear algebra and regression and PCA before, so was able to complete it rather quickly, but this should be very approachable and useful for everyone.

par Jatin k

•Jan 02, 2020

A very good course for beginners who want to study machine learning. Mr Andrew Ng is a very good teacher and very experienced in machine learning. The course structure is what it should be for an ML course. Programming exercises are really brainstorming and must be solved. Online threads can be used to seek help from other students and mentors, and are really effective. Reading slides are important for making notes.

This course is a very good and effective platform to learn machine learning skills.

par Subham

•Mar 03, 2019

The real way to learn Machine Learning is this, no black box;understanding using pure mathematics makes it more interesting, and as I was solving the programming exercises I got to know, how simply vectors and calculus can be used to represent complex mathematical formulas. All the hours completing this course was worth. Once I started using machine learning libraries, all concepts were no longer black box for me, suddenly everything started making sense. Highly Recommended course for beginners.

par Martins R

•Apr 24, 2019

This was the hardest thing I've done in ages. I gave up at some point until a breakthrough in programming - I learning to use operations with matrices. I did all programming assignments in python. Couldn't finish the Neural Network - I was stuck for a month because I couldn't wrap my head around mathematical operation in backpropagation. Overall this was a journey. Every morning and evening learning on the way in the bus to and from work. Also lonely weekends. Finished. Can't thank you enough.

par Manish S

•Feb 04, 2020

It is an amazing course for beginners who wish to know about Machine Learning. Taking the course and getting high-level knowledge of how different ML Algorithm works can be very useful and (in some cases, it is must) before using any libraries to create solutions. And for such cases, this course is certainly one of the best.

I sincerely thanks to Andrew Ng for taking out his time to make this course for a student like us. I highly recommend anyone to take this course with no hesitation.

par Emily C

•Jan 02, 2020

A great introduction to Machine Learning. Found the pace of the lectures just right with a good balance of theory, worked examples and practical tips. I did Maths with Statistics at university and so found some of the concepts familiar but great to refresh! The coding assignments were well-explained and was able to walk through them step-by-step with the instructions. Really enjoyed the course and excited to start testing it out on some problems of my own!

par Vaibhav J

•Jun 05, 2019

The explanation of each and every topic is so simple and easy. The course is taught by prof. Andrew Ang and covers the major concepts of machine learning. He also provides a good intuition about the topic so to understand them better. Overall this course is awesome and I would highly recommend to someone who is a beginner in Machine Learning. I am very grateful to Professor, Mentors and the Coursera for this amazing journey of 11 weeks in machine learning.

par Miklós L

•Jan 15, 2019

Amazing class, great lecturer. A really good introduction and overview of machine learning concepts. It often skips the detailed mathematical background, to make it accessible for a wider audience, but I still found that enough information was given that allowed me to work out these details on my own. A lot of effort was put into creating the programming assignments, they provide a great hands-on experience with machine learning algorithms.

par Harshit A

•Jan 13, 2019

This course is a really amazing and well taught course.Andrew sir is really a very good teacher and he made the complex topics quite easy to understand with his cool examples.Moreover, the optimization techniques and advises given for debugging or improving the machine learning program were really helpful. I hope I can take full advantage of this course and build up a career in machine learning.

par Emmanuel N

•Dec 06, 2018

Amazing course. I had no idea of programming and my maths were more than rusted, but the way the lessons are taught, made the way a whole lot easier. If you're like me (zero programing and maths), it's no easy task to complete the course. But if you put the right amount of effort, patience and dedication, combined with the great videos and reference material, is totally doable.

par Shashank S

•Sep 09, 2019

This course was splendidly awesome. I am in my final year from grade C engineering college in New Delhi, India. This course helped me a lot to understand about the basics and gave me deep understanding about the field. Thanks to Andrew NG Sir who made this great website for students like us and such an best class content. Highly Recommended to all!

Thank You!

par Asmita P

•Apr 08, 2019

Hi Andrew,

I liked this machine learning course so much. I enjoyed doing all the assignments. I am working in IT industry. I was thinking of working in a new age technology and then my brother told me about this course as I love maths and programming.

I am looking forward to get deep knowledge in machine learning.

Thank you,

Asmita Patil

par Panu M

•Jan 02, 2019

Very eye-opening for a person with a very little knowledge of the aspects and maths behind machine learning. The exercises were somewhat difficult since it's been 15 years since my last maths class and I really haven't been doing it since. So a lot of effort really needed, but once you've done it, it feels great!

- L'IA pour tous
- Introduction à TensorFlow
- Réseau de neurones et deep learning
- Algorithmes, Partie 1
- Algorithmes, Partie 2
- Apprentissage automatique
- Apprentissage automatique avec Python
- Apprentissage automatique à l'aide de SAS Viya
- La programmation en R
- Intro à la programmation avec Matlab
- Analyse des données avec Python
- Principes de base d'AWS : Going Cloud Native
- Bases de Google Cloud Platform
- Ingénierie de la fiabilité du site
- Parler un anglais professionnel
- La science du bien-être
- Apprendre à apprendre
- Marchés financiers
- Tests d'hypothèses dans la santé publique
- Bases du leadership au quotidien

- Deep Learning
- Le Python pour tous
- Science des données
- Science des données appliquée avec Python
- Bases de la gestion d'entreprise
- Architecture avec Google Cloud Platform
- Ingénierie des données sur Google Cloud Platform
- Excel à MySQL
- Apprentissage automatique avancé
- Mathématiques pour l'apprentissage automatique
- Voiture autonome
- Révolutions Blockchains pour l'entreprise
- Business Analytics
- Compétences Excel pour l'entreprise
- Marketing numérique
- Analyse statistique avec R pour la santé publique
- Bases de l'immunologie
- Anatomie
- Gestion de l'innovation et du design thinking
- Bases de la psychologie positive