Retour à Apprentissage automatique

4.9

étoiles

151,335 évaluations

•

38,609 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....

HS

2 mars 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.

TP

25 juin 2020

This course is a very applicable. Professor Ng explains precisely each algorithm and even tries to give an intuition for mathematical and statistic concepts behind each algorithm. Thank you very much.

Filtrer par :

par Germain M P

•5 déc. 2019

Poor audio and video quality, what compromises the learning process

par Reinhard H J

•18 oct. 2019

The course content is vastly outdated and superficial.

par Anand R

•20 nov. 2017

To set some context: I am a graduate (PhD) in Computer Engineering from the University of Texas at Austin with over 10 years of experience in both academia and industry. My goal in taking this course was to learn the basics of Machine Learning, and understand what the current excitement about ML and AI is all about. I dedicated 3-4 hours every week, over the last 12 weeks, towards learning this course — and watched all the videos, reviewed all the lecture .pdfs and completed all the project assignments and all quizzes in the course on time.

About the course: This is one of the best courses I have taken (and I have taken more than 10 courses on coursera, edX and Udacity). Dr. Andrew Ng needs no certificate of approval from anyone. He is clearly a wonderful teacher, and I felt I struck a chord with him. There are few people who can explain complex concepts clearly without over-simplifying. Some people don’t have the ability, and often those who do, don’t care enough. The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down. Dr. Ng does that very well in several places and my deepest respects to him for doing that.

Clearly, Dr. Ng is a pactitioner in the field. The material was very well structured, very well paced and presented in bite-sized modules. The project assignments were both challenging and quite realistic. I feel a tremendous sense of confidence having completed this course, and I hope to try out some ML challenges on the web in the near future.

Last, but not the least, I cannot appreciate Dr. Ng more for the effort and dedication he has put into the subject and into his teaching. I felt a touch of nostalgia as the course ended suddenly with the last video (which was very moving, btw) and there was no NEXT button to click on. Being an educator myself, I know it takes a LOT of time and effort in developing a course. After completing this course, I felt I owed it to Dr. Ng. to purchase the course. I feel proud and happy to be certified as his student.

Thank you, Dr. Ng.

Thank you coursera.

par Irfan S

•6 avr. 2020

Extraordinary course for beginners (as well as for people with experience)!

If you are a beginner (as was I before taking this course), then this course is the perfect way to start learning Machine Learning. Even if you have some experience with ML, it'd be useful to learn about the recommended practices for choosing the right approach for a problem or something like debugging an algorithm.

Dr. Ng presents a huge amount of information in a structured manner, bundled with questions within videos that keep you focused. The quizzes and programming assignments complement the lecture videos. The programming assignments are in Octave. This is not necessarily a negative point (as other reviews are saying). If you are familiar with Python (or C/C++/Java etc), then it won't take you more than a few days at maximum to grasp the syntax of Octave. There is a lot of helper code in the programming assignments, so you mostly focus on the actual implementation of algorithms and such. Dealing with vectors and matrices in Octave has been a relatively better experience for me as compared to in Python. If you're stuck with programming exercises, then there are elaborate tutorials in the Resources section.

Possibly what I loved the most about this course is how Dr. Ng always mentions the recommended way of doing things (and how things are done in the industry). He also teaches you real life examples of how ML is currently being used by companies (for e.g. the course weeks on Recommender systems, Photo OCR, etc). So, if you're trying to learn ML for job prospects, this will be of great help.

Even though there's a fair bit of math (Linear algebra and some Statistics), Dr. Ng will help you walk through it and make you understand what you need to know.

Overall, this course has been a great help for a beginner like me. I recommend this to anyone who is looking for a course to start learning ML.

To Dr. Ng, the mentors of this course, and all the people who made this course possible, I want to thank you from the bottom of my heart. It's not easy creating so many hours of content (lecture videos, quizzes, assignments) and providing it online to thousands of people. I'm grateful for all your efforts.

par Kevin M

•13 déc. 2019

This is a terrific class! The Course is well structured in terms of videos, invideo pop-up quizzes, course notes, programming exercises, and the discussion boards & mentor community. The 11 weeks includes 8 programming exercises, with usually 5-6 "code submittals" per exercise.

The option of OCTAVE or MATLAB is great (I used MATLAB). A key aspect of this course is using vectorized methods in every programming assignment. There was always an option to write a procedure approach (e.g. do loop for summation steps like sum of squared differences for gradient descent or linear regression). The computational advantage, the simplicity of using vectors, and ending with "crisp" code is a great step

I have completed a similar class from MIT (Python or R based) and the exercises in this class were far superior in reinforcing the course materials.

This journey takes you through Supervised Learning models leveraging Linear Regression, Logistic Regression, Neural Networks, and Single Vector Machines and how gradient descent is the cornerstone to determine the theta values needed to optimize your hypothesis. Unsupervised Learning using K-means, PCA, and Anomaly Detection. Specific real life example for Recommender Systems, Character Recognition and large scale machine learning.

The various topics on "advice" by Professor Andrew Ng is invaluable. Understanding how to measure performance of your algorithm is key. Underfitting (bias) and over fitting (variance), regularization, learning curves, evaluation (precision, recall, and F1), and error analysis. Of particular note, is his understanding how to objectively determine how to what to work on next and how to apply "ceiling analysis" in complex pipeline ML applications.

A final note, the course mentors are unbelievable! Tom Mosher and Neil Osgrove are truly special. Their understanding of the material, their patience, and their incredible responsiveness is highly beneficial to the learning experience. You have to do the work and figure it out, but the mentors are there to help you navigate the Machine Learning journey!

par Boquan Y

•18 mars 2020

Really a great course. It covered a large variety of currently popular machine learning algorithms, along with strategies to do machine learning projects. Professor Andrew really goes deep into how to optimize a machine learning model to reduce bias and improve performance with a lot of techniques, not just simply implement a fancy machine learning algorithm. At first, I complained about programming assignments because it is done in Matlab, but after I went through some of them I really discovered that Matlab is a powerful tool used for a broad range of purposes. The course goes beyond just model.fit(x,y) and model.predict(x,y), because you'll learn the essence and mathematical proof of each ML algorithm to really comprehend how each algorithm work and how optimization work. You can still learn to build ML models in python even by yourself after this course.

However, there are still some problems I want to mention. First, for some algorithm in the second half of the class (e.g. SVM with Gaussian kernel, anomaly detection), professor Andrew didn't sufficiently mention how math works, just giving the conclusion of how we should implement. I understand that maybe it is because the mathematic proof is too complicate here or it is not necessary to know the mathematic for mastering this type of algorithm. But I still hope that I can have a deeper understanding of every model based on mathematics. Another thing is that programming assignments didn't teach us how to plot graphs. Our work is only limited to "backend" implementation, which is the completion of the algorithm using a mathematical approach. I still hope Professor can introduce how to plot different kinds of graphs to really integrate our knowledge on "backend" to "frontend" for further data analysis.

Again, this is a great course, and anyone who completes this course will gain a lot of insights on ML and will have a solid understanding for future ML studying. Thank you, Professor Andrew!

par Anuradha R

•24 mai 2020

I knew nothing about Machine learning when I started this course. I am going to start a job where I have to verify hardware for machine learning and I wanted to understand the vocabulary of machine learning better before beginning this new job. I got that from this course and a lot more! I liked the balance of mathematics, modeling and hardware aspects of this course. A key aspect of this course that elevates it is how Andrew always emphasizes evaluating the model / algorithm with real number outputs and not just plug ahead at full speed.

Thank you Andrew for putting this course together and making it accessible to all. I know how difficult it is to take a complicated topic that you are very conversant with and explain it in a way that a person not very familiar with the field understands it. And Andrew nailed this aspect.

This was also the very first course I have taken on Coursera. I am now inspired to try many more courses. Using Coursera to learn new concepts from home, without the pressure of time, money and grading is an incredibly liberating idea for me.

Overall, my experience with this Course and Coursera for me has been a 12/10.

par John H

•22 août 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

•11 août 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 Yintao L

•20 juin 2020

This course is an intro-level course for Machine Learning which mainly focus on the implementation of those algorithms. It doesn't mention much math behind which makes it suitable for people even have no previous knowledge in related area. But make sure you have at least basic knowledge about linear algebra and calculus. You won't need those for the exercises but would help you better understand the course.

The exercises are really helpful for students to understand the material. If you want to learn more, I deeply suggest you not only finish the required exercises but also the extra exercises for each week.

Besides that, Professor Andrew's explanation and illustration is really clear and easy to understand. Even though this course has been online for many years, it contains the knowledge that still practical nowadays.

Overall, Five star course and I strongly encourage people with little or no background knowledge but aiming to learn about machine learning start with this course.

par Ozgur U

•6 janv. 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

•30 août 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

•19 janv. 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

•5 juin 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 Lubin Q

•16 août 2020

As a non-CS student, I really have learned a lot from this course, which does not only cover several typical algorithms, but also a lot of important concepts in ML. It can be told from all these lecture videos that Prof. Ng has put a great effort in this course - he is not just reading the pre-prepared materials; instead he has sincerely shared a lot of his experiences in industry and pointed out the typical pitfalls that a lot of ML engineers have fallen in. This really inspires me and lets me develop a lot of awareness to avoid similar mistakes in the future.

Although this course is not the end of ML study, it is an excellent course as an introduction of ML for beginners to start with. Thanks Prof. Ng and your mentor team for all your efforts.

par Christian D

•18 août 2020

Excellent introduction class to ML! Prof Ng provides clear explanations always and makes Machine Learning simple. I have learned to go with the flow of the videos, not worrying when I was not understanding some parts knowing that a clear explanation would be provided in the following minutes. Although this course is not interactive, Prof. Ng communicates well with his passion, and always "responds" to my questions in the videos. The quiz and exercises are very well thought of, really testing that we learn the essential and got a good feeling for the concepts.

Thanks to Prof. Ng for this excellent class.

(note: I would be interested in a follow-up class on Machine Learning, is there another class from Prof Ng avaialble soon on Coursera?)

par RENZZO S

•29 oct. 2020

Excellent course for a depp introduction to machine learning. The professor Andrew NG has a special way to explain complicated themes in a very simple and understandable way. In the main videos of this course is more intuition than deep math and statistical demonstrations, but if you eager to understand issues more deeply like me you will find in the "resources" area of the course links to the documentation and the lecture videos of the machine learning course given in Stanford, there you could find the math and statistical demonstrations, also a bunch more algorithms to learn. Also you will find links to refresh your calculus, linear algebra and statistical skills if needed and links to data repositories to practice your new skills.

par Arpit J S

•1 mai 2020

Mr. Andrew Ng has mastery on Machine Learning. His method of teching is precise and lucid, often engaging us to think more on untouched aspects of ML. This was my first course and first step (a baby step) on any platform to understand and learn ML . Lucky to have enrolled for this amazing course and I sincerely thank him for being instructor on this subject and also tons of thanks to mentors who clear doubts in discussion forums. It helped a lot. Lastly , I think this course has clearly set my path towards advanced studies in ML. Although, statistics and some of the terms did bounce off my head few times, I hope to revisit and work on them more in future. Thankyou Andrew Ng Sir ! I am your fan now !!! :)

par Amirhossein B

•3 juin 2020

I so appreciate it from COURSERA and DR ANDREW NG for this unbelievable course. It was definitely one of the best courses I've ever seen in my whole 20-year life. I'm from Iran and I have really restricted rules for having access to such courses. I'm so glad to have this opportunity to attend a class with a professor from Stanford University. I'm not good at English very well but I don't know why I feel that at the end of the class Prof NG was kind of sad from ending the course and I was nearly to cry seeing him like this. here I'm gonna promise this for the first time, I promise to spend my whole life to do what Prof NG did for me in this course, to help others. Thank you so very much.

par Vincent C

•25 sept. 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

•6 juin 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

•11 mars 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 Tun C

•2 févr. 2018

I've been working with machine learning for a while and I've used different supervised and unsupervised algorithms. However, this course taught me about how these different machine learning algorithms work under the hood. Professor Ng is a great teacher. His method of describing the problem set, giving the intuition on how to go about solving the problem and slowly defining the algorithm works very well. This course has the right amount of breadth by covering only the most applicable algorithms and has the right amount of depth by covering the math and the intuition behind each algorithm.

par Anith S

•6 juin 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

•23 févr. 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.

- Recherche d'un but et d'un sens à la vie
- Comprendre la recherche médicale
- Le japonais pour les débutants
- Introduction au Cloud Computing
- Les bases de la pleine conscience
- Les fondamentaux de la finance
- Apprentissage automatique
- Apprentissage automatique à l'aide de SAS Viya
- La science du bien-être
- Recherche des contacts COVID-19
- L'IA pour tous
- Marchés financiers
- Introduction à la psychologie
- Initiation à AWS
- Marketing international
- C++
- Analyses prédictives & Exploration de données
- Apprendre à apprendre de l'UCSD
- La programmation pour tous de Michigan
- La programmation en R de JHU
- Formation Google CBRS CPI

- Traitement automatique du langage naturel (NLP)
- IA pour la médecine
- Doué avec les mots : écrire & éditer
- Modélisation des maladies infectieuses
- La prononciation de l'anglais américain
- Automatisation de test de logiciels
- Deep Learning
- Le Python pour tous
- Science des données
- Bases de la gestion d'entreprise
- Compétences Excel pour l'entreprise
- Sciences des données avec Python
- La finance pour tous
- Compétences en communication pour les ingénieurs
- Formation à la vente
- Gestion de marques de carrières
- Business Analytics de Wharton
- La psychologie positive de Penn
- Apprentissage automatique de Washington
- CalArts conception graphique

- Certificats Professionnels
- Certificats MasterTrack
- Google IT Support
- Science des données IBM
- Ingénierie des données Google Cloud
- IA appliqué à IBM
- Architecture Google Cloud
- Analyste de cybersécurité d'IBM
- Automatisation informatique Google avec Python
- Utilisation des mainframes IBM z/OS
- Gestion de projet appliquée de l'UCI
- Certificat stratégie de mise en forme
- Certificat Génie et gestion de la construction
- Certificat Big Data
- Certificat d'apprentissage automatique pour l'analytique
- Certificat en gestion d'innovation et entrepreneuriat
- Certificat en développement et durabilité
- Certificat en travail social
- Certificat d'IA et d'apprentissage automatique
- Certificat d'analyse et de visualisation de données spatiales

- Diplômes en informatique
- Diplômes commerciaux
- Diplômes de santé publique
- Diplômes en science des données
- Licences
- Licence d'informatique
- MS en Génie électrique
- Licence terminée
- MS en gestion
- MS en informatique
- MPH
- Master de comptabilité
- MCIT
- MBA en ligne
- Master Science des données appliquée
- Global MBA
- Masters en innovation & entrepreneuriat
- MCS science de données
- Master en informatique
- Master en santé publique