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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....

TD

30 oct. 2021

Thank you very much for the excellent lectures. I am just wondering about the back propagation algorithm. When we calculate the errors backward, why do we use matrices theta instead of their inverses.

RK

19 août 2019

It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

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par Cornelis D H

•13 déc. 2017

An approachable introduction I recommend to anyone and everyone that at the least has matrix algebra under their belt. I took this with the intention of reviewing material, yet many concepts I had previously been exposed to were approached in such a clear manner that it felt brand new to me. I'm shocked at how much more clarity this course offered than my university! Moreover, it is absurd that I did a lot more work in my machine learning university course, yet never saw it pay off in an interview. This course was far less work (i.e. less time-consuming), but the pay-off knowledge-wise for interview questions has been demonstrably far far more time-effective. I highly recommend this course if you are limited on time and want a course to give you an edge in interviews. The big downside of this course is that there is no term project. In my case that was fine since I already have ML projects under my belt and needed to review all ML concepts at a general level, but if you don't have any noteworthy ML projects and are looking to dive into one, I'd still highly recommend this course (so many problems I can point out now with my ML projects in interviews for instance!!) but know that you'll want to move onto another project-focused course or a personal ML project after this for sure to get yourself ML-interview ready ;)

par David C

•6 août 2017

This is by far the best Coursera (or any MOOC) class I've taken. The production values are low, but the content is excellent. Professor Ng explains everything very clearly. The best part is the homework. Each assignment is explained in detail and the assignments lead you in small steps to make measurable progress as each step is accomplished. The 5-6 page PDF that accompanies each programming assignment provides all the information needed to fully understand what you are being asked to accomplish. I have had MANY other MOOC's where the information in programming assignments amounts to a few sentences embedded in a Jupyter notebook and students are forced to search through numerous forum questions and answers to get an explanation of what you are really being asked to do. Not in this course! Everything is explained very well. I might also mention that while most other machine learning courses are taught at a very high level, using Python and Scikit learn or other ML packages that do the heavy lifting, this course has you actually implement ML programs at a very low level, using linear algebra and vector math so that you really get to see how these higher-level packages implement the detailed algorithms. No black boxes here. I HIGHLY recommend this course to anyone interested in machine learning.

par Ritwik D

•19 oct. 2019

First of all I'm so thankful to Andrew NG for this wonderful course and I'll always be in debt to him for this masterpiece. At the very start, when I was just starting out, I was really unsure whether this would be the right course for me as this is an 11 week course. I was also concerned about the usage of MATLAB/ Octave over Python. But choosing this course, I realized it didn't really matter given that you know how an algorithm is working under the hood. I love how Mr. NG has the special ability to teach such complicated topics with so much ease. I can never expect the same from any faculty I've personally come across. Yes, the programming exercises were sort of a pain but there are github repos that let you do the same in Python. What I've learned over these 11 weeks, I believe, is immensely valuable and probably very few courses out there are capable of that. This perhaps has been one of the best decisions of mine so far. I plan on moving on to deep learning after this, thanks to Mr. Andrew NG.

Also, over this course of 11 weeks I felt attached to Mr. Andrew NG and I can proudly say that he's been the best teacher I've ever come across. It was a little hard to watch the last and final video knowing its the end of the road. So thank you sir for this wonderful work of art! I'll forever be in debt.

par Vincent B

•29 juil. 2017

This is my first online course and I am so happy I selected Andrew's Machine Learning course as my first. The material was well presented, provided plenty of information about why and how you should use each Machine Learning method and importantly he spends time time providing the intuition for why certain mathematical formulas are used. The quizzes were challenging and gave you ongoing feedback on wether you were grasping the material in each section. When you get one of the questions wrong it helpfully points you back to the appropriate material in the lectures to review. Finally, I would like to thank Tom Mosher, a mentor and the author of most all the programming assignment tutorials and a constant presence and help on the discussion boards. I didn't have to ask many questions but that was primarily because someone else already had and Tom provided the answer, often times within minutes or hours. Even though I have little experience with online courses, the amount of support and attention to detail throughout demonstrates how good this course is and I can only hope is held up as a model for other courses. Thank you Andrew and Tom. I am truly grateful and can confidently say it was 10 weeks of work well worth my time and the knowledge that I gained will be used at my employer immediately.

par Cameron P

•21 juil. 2017

This is an excellent course covering the fundamentals of machine learning with a wide breadth of subjects as well as, in my opinion, a reasonable depth into each as well. My only major qualm with the course (and this is just personal preference of what I would like to have learned) is that the programming language used for this course (that you must do the assignments in) is Octave/Matlab. As Dr. Ng mentions in the class, Octave/Matlab is an excellent way to prototype your machine learning algorithms, and after having used it for this course I agree. However, I believe it would have been much more useful (personally) to do the programming parts in a more industry standard language for your final product such as python. Point being, having completed the course I feel that before I can apply this as a final solution I must still learn how to implement these algorithms in a different language with different libraries, so it doesn't feel quite as if my journey is 100% finished. That being said, I do feel that Dr. Ng gave a great mathematical understanding of the algorithms and I do not believe that it will be terribly hard to implement them in another language. I believe the only barrier would be learning the Matrix Algebra and other ML related libraries standardly used in that language.

par Thomas T

•14 mars 2016

Hi. This is a great course which would be made even better with the small change I describe below. I was referred here by Nicole who was handling this as Support Case #1188060. I understand the mentors have been seeking this improvement for some time. Please let me know if I can clarify or assist in any way. Thanks in advance.

---- Support Case #1188060 ----

Hi. I'm writing regarding the Machine Learning course taught by Andrew Ng of Stanford. I have learned there is a compilation of "errata," known errors in the videos and course materials for each week, which has been made available in per-week links like the following:

https://share.coursera.org/wiki/index.php/ML:Errata:_Week_7

I found these links only after digging through discussions. In my own discussions afterwards, I learned that I wasn't the only one having trouble finding the errata. Apparently it's a long-standing problem which creates extra work for mentors as well as slowing down students. Would it be possible to provide the errata links, or a link to the Machine Learning Course wiki, in a dedicated section of the Course Content? This would help a lot of people. Please see the discussion below for more perspective.

https://www.coursera.org/learn/machine-learning/discussions/RQNTlee5EeWTdBIkpCpI1Q

Thanks for your consideration.

Tom :-)

par Demitri M

•14 août 2016

Gently-paced and reasonable in terms of course work load, even for a busy person. This course greatly demystified machine learning for me.

Alvin's lectures are friendly and easy to follow, and progress smoothly from one point of interest to the next, always taking time to briefly recap on precursor topics that were previously discussed.

I really appreciate how the assignments focus on the core algorithms and their implementation. Ancillary implementation steps and a scaffolding for testing and viewing the results are already given for the learner to use, play with, and go back and reference for educational purposes if need be, for refreshing on use of tools such as plotting methods. The way the assignments are structured, putting emphasis on filling in the gaps with implementation of the important algorithms, makes completing assignments a very satisfying and fulfilling experience. I feared I would get caught up in preliminary steps, i.e. getting plots to display correctly or cleaning up the data, but that's all taken care of so that one can focus on practicing the techniques most relevant to the topic and algorithms.

I only wish there were more assignments to complete, both to cover more of the course's material and to have more chances to practice algorithm prototyping in Octave/Matlab.

par Maury

•13 mai 2016

This course in Machine Learing was challenging, but do-able... and very rewarding. This is the first on-line course I've taken. It has been many years since my last formal college course, so I was a little rusty at the math (I'm 55 yrs old, ancient compared to my peers in this class. My last math class was over 3 decades ago, probably before most of my classmates were born. Damn, I'm old). Even so, with Mr. Ng's teaching style, I was able to finish the course with both a good grade and - better still - a good grasp on the subject material. I will certainly continue to pursue additional learning in this area.

Machine Learning is a fascinating field. I was interested in this area way back in the late 80's when I first heard the term "Neural Network", but there were no on-line courses in that pre-internet era, so the availability of information about the subject was very limited. It was mainly in breathless magazine articles about the coming "AI" revolution. It took a few decades for that promising spark to mature into a practical reality. Now, with the appearance of Coursera and other on-line education portals, much of this information is readily available to all with the curiosity and perseverance to learn about it.

I am grateful for this opportunity to learn about Machine Learning!

par Rahul K

•22 déc. 2017

An exceptional journey through this vastly mind-boggling domain that is Machine learning. What a ride it has been! I must admit, before I started this course, I was pretty skeptical about it. Back then, Machine Learning seemed like magic to me. Only after this course did I realize that Machine Learning isn't as intimidating as it seems. In fact, if you have basic arithmetic skills, you can learn Machine learning to a very reasonable extent. Prof. Andrew Ng, hats off to you! You have cemented yourself as a role model for me since these past few weeks.

Just as an aside, I wanted to let all the readers know that I am working a 9-5 job in IT. Not to be a humblebrag, but seeing that I took time out to complete this course just goes to show that if you put significant effort and planning, you will be able to complete this course (rather, any course) in the stipulated time frame.

The curriculum that this course offers is challenging - You don't get spoon-fed answers when you want them. Just a little hint - Pay very close attention to everything Prof. Andrew says in the lecture. Don't open other tabs and listen to the lectures in the background.

Overall, a fantastic course! Very excited about what ML holds in the future. Prof. Andrew, we need more series from you! Thank you so, so much!

par sandeep m

•8 oct. 2020

An excellent course.

Helps one Understand the core foundations of machine learning and the learning machinery.

How do algorithms learn from data and produce models (Generated code representing a complex math function, tree, forest or a Neural network)

The above skill is very important as compared to just the application side of ML. The application side generally involves mapping use-cases to certain ML-API's.

This translates further to Rote learning (cramming) all the various ML Api's offered by various frameworks, vendor products, and cloud frameworks (Scikit learn, TensorFlow, Spark ml, PyTorch, sagemaker ..........)

End result being, the problem is solved, but without developing a thorough understanding or deep intuition about how it was solved?

It was an arduous effort, as one had to pull oneself after the day job every alternate day or over the weekends. There were numerous hair-splitting frustrating moments that tested one's tenacity.

But the end result was very satisfying and gave a sense of accomplishment. It was fun working on the exercises, designing an algorithmic approach and coding it in Octave.

A very good course that builds the foundation, pushes one's analytical thinking to limits, activates and tunes the grey cells. (The real neurons, pun intended)

par Benedict W

•16 juin 2017

Amazing course. I'm on the founding team of a payments startup, where my role consists mostly of sales, digital marketing, and 'growth hacking'. At university I studied law so I had completely no knowledge of linear algebra or post-high school level mathematics, or programming. Yet I found the material digestible and engaging. At times the material was very challenging but I was usually able to understand after rewatching the videos several times and/or Googling alternative explanations. The programming exercises were not easy for someone with no programming background, but again, Andrew Ng did an excellent job of teaching the basics thoroughly and efficiently. The forums have some useful tips on completing the programming assignments in case you get stuck, which I did make use of occasionally. I did find that towards the end of the course, as I started becoming much comfortable with Matlab syntax and programming logic, I was able to complete most of the assignments without having to look for help in the forums.

All in all, this was a phenomenal course for anyone interested in machine learning. It takes a lot of patience and time to work through all the material and programming exercises (probably 100-150 hours for someone with no stats / CS background) but well worth it!

par Danilo D

•7 sept. 2020

An absolutely amazing course!

The lectures are well structured, they go over all the necessary theory for understanding the machine learning algorithms presented and also contain a lot of great examples and illustrations that genuinely help learners better understand the usage of the ML algorithms.

Plethora of materials are at disposal to students: programming test sets, additional literature in the form of suggested textbooks and lectures, and probably one of the most helpful resource - the forums, where Mentors are quick to answer any questions that students may have - something I found very very helpful in enhancing my learning process.

The programming assignments are very good - they are challenging, but also serve as a great learning tool that shows students how all these algorithms we learned about in lectures are actually implemented in practice. They really help students deepen the knowledge, provided they take the time to figure out the solutions themselves. The quizzes are fair and ask from each student to truly understand the matter before answering the questions.

All in all, I would say that the course is very well worth the time invested, and I would gladly suggest anyone who wants to learn about Machine Learning to go ahead and go through this course!

par Amine M L

•27 mai 2021

Thank you Coursera , Stanford and of course Sir "Andrew NJ" for making this possible . I have been Using Techniques such as Regularized Logistic Regression , SVMs , Clustering , and Deep Neural Networks for many years without giving much thought to the intuitions and rationales underlying some of their key concepts . Not anymore . This course did just that for me . Among many other things , This course does particularly a fantastic job Explaining the nuts and bolts the "Bias-Variance" Trade-off , why Regularization is needed , The importance of cross validation and testing , Batch/Mini-Batch or Stochastic Gradient decent , The regularization terms , The Learning rate & Momentum, and how one should go about debugging an algorithm using the learning curves , Cost-Function Graphs and Error Analysis Techniques . All in a unique an' immersive hands-on experience . It even makes the much revered Vectorized Cost functions , Maximum Likelihood-based approaches , Convergence in Probability , Vectorized-implementations of Forward and Back Propagation (an' more) , look like good old friends . I personally had lots of "EUREKA" moments throughout the journey . I absolutely recommend this course to anyone who wants to refine & solidify their Machine Learning Foundations .

par mss3331

•23 sept. 2019

This course is suited for you If you don't have a background in Linear Algebra and Machine Learning.

However, if you do have a background in Calculus and some Linear Algebra you will understand the intuition better (i.e. You can go to the lecture notes for the proofs and math explanation.).

I rarely pay money for a course , but this one worth my money! Specifically, the programming exercise were so helpful to fill in the gaps and give you a real understanding of the concepts been explained. At first you will struggle with the exercises (specially if you don't have a background in MATLAB), then you will get to used to it (i.e. you will ended up solving an exerciser in one day if you are used to it). Added to that, these exercises can be used to create your own Machine Learning Systems. In fact, I was sad that the last two weeks has no assignments. Also, you can learn from the written code in the assignment to visualize your own data. Finally, those exercises are what make the course balanced between the theory and practice! My advice for you: "If you have the money, go for the paid course. You will benefit from it even after you finish!"

I would like to thank Andrew Ng for his effort explaining the concepts and giving me the courage to continue further

par Carsten P

•10 oct. 2016

About me: I studied computer science in Dortmund, Germany in the 90ies. I recommend this course to everyone who wants to have a very good understanding of machine learning. A little bit of advice, if you have never learned linear algebra on a university level, you should at least try to get a basic understanding of it before starting this course. I was happy that I remembered stuff, learning it from scratch in 1 or 2 weeks would be difficult, I assume.

+:

* Mathematical basics of machine learning are very well explained

* Andrew Ng is a very good professor, he explains the topic very well and thoroughly

* It is not limited by using a special framework or language

* The support in the forums, and the transcription of the talks, and all the material that is given to you is really excellent.

-:

* I would be happy if the programming exercises would be a bit more fun, currently it feels like translating / transforming math formulas into octave, which is fine, but not very fun. Having said that I am only in week 4, perhaps this will happen later

* some text questions in the multiple choice quizzes require a precise understanding of the english language, especially in regards to math, I am not a native speaker, so these questions feel especially hard for me

par Joshua W W O

•28 juin 2017

This is my first online course that I have ever completed and this feeling of completion is so immense! It took me one year to complete this. This was because I studied it part time while working and at the same time had other commitments pop in along the way. Nonetheless I'm really glad I made it through.

I would recommend this course to anyone who would like to learn about machine learning. It gives you strong foundations into the subject. You will realize right from the beginning of the course what machine learning is really all about. Though some of the assignments were quite tough especially on Neural Networks but you will eventually figure it out. Once you do, the feeling is tremendous! You will learn much about machine learning in two main aspects in supervised learning namely regression problems and classification problems. There is also unsupervised learning whereby you learn to form some sort of structure (patterns) in a dataset using K-means and also detect anomalies (e.g. fraud detection) via anomaly detection algorithms. You will also gain tools on how to analyze the performance of your system and what should you do next such that you will best make use of your time. Overall, this is a fantastic course! Thank you Prof. Andrew Ng!!

par pandenghuang

•11 déc. 2020

This is really an amazing online learning experience for me! Thanks a lot, Andrew and your team!

I bought a popular machine learning tutorial in 2017 and tried multiple times to understand what on earth machine learning is about, but always found that there is a huge gap between my knowledge and the content described in the tutorial.

But I didn't give up. I spent lots of my time learning Advanced Mathematics, Linear Algebra, Probability and Statistics and went back to the tutorial, but unfortunately it was still very difficult for me to understand the tutorial.

It is only after I participated this great online course: Machine Learning by Andrew Ng, that I got a feeling that this time I can make it through. I watched the videos carefully, sometimes again and again trying to catch every words. I completed all quizzes and in-video questions and found that they are quite helpful for better understanding of the course content. I spent hours trying to complete the programming exercises and learned a lot. I may never forget the exciting moment to submit my homework for the first time, online in Octave command line.

Thanks again for your guidance! Will keep learning and make best use of this learning experience in my future work and life!

par Kumaresan

•12 oct. 2015

a. very good coverage of standard algorithmic approaches.

b. good suggestive guidelines on specifics of algorithms like issues / details one need to be careful, need not to bother etc..

c. broad coverage of examples..

d. tricky questions...good to experience...

Overall I liked this course content and the breadth of coverage. Based on the difficulty i experienced let me place some points of improvements that would help every student....

e. could have dealt some specific examples in full (from definition to implementation) as part of video lecture which would helped better understanding of the problems, algorithms, impact of specifics, implementation issues, analysis methods, inferences that could be derived, final expected solution.

f. expecting feedback on exercises.... not only correct or incorrect but reasoning for the responses could be of great help in better understanding....

g. downloadable videos could contain in video quiz...

h. Octave content could be increased.....

i. audio of the lectures needs fine tuning, hissing sounds could be filtered. For some of the lectures subtitles does not match at all...

Thank you very much for coursera....

Thank you very much Prof. Andrew Ng.....

Looking forward for mor courses related to ML by you....

par Michael K

•3 mai 2020

The course specifically I rate at four stars. It's a great course, and I learned a lot from it. It's not afraid to get into more advanced details and mathematical underpinnings. The quizzes and particularly the programming exercises will challenge you. Professor Ng clearly knows his stuff. However, there are some serious flaws:

1) Audio recording quality is not good and sometimes he can be hard to understand.

2) Tons of errors in the videos made note taking a pain. The errors typically aren't highlighted and explained until the reading material that comes after the video. So my notebook has quite a few scratched out lines due to mistakes in the videos that are only later corrected.

3) Similar to #2, there are quite a few inconsistencies in the way formulas are expressed. Terms get moved around or written slightly differently often with no explanation. It can create some unnecessary confusion.

I still gave the course 5 stars though, because the assistants on the forums are absolutely excellent. They answer almost every question students have and take the time to explain details and intricacies of the algorithms. Their tireless dedication to helping students more than made up for difficulties caused by mistakes in the videos.

par Adarsh K

•24 mai 2019

The best Introductory Course on ML ever. No Pre-requisites allows anyone with the interest to learn ML learn it in the best possible manner. The course not only gives the Theory but also develops Intuition behind every algorithm which helps to retain the essence of the entire material. Not only the Theory but also the Practical Advices that the prof gives helps you to implement a ML Project from Scratch and Diagnose any possible error that may creep in, some of which aren't even used by many Industry Professionals. The prof is very humble and teaches you more like a friend, giving examples on how simple things may go wrong, also accepting that some of the concepts are not so easy to digest, so don't worry. The course is superbly organised which helps learners learn everything that the instructor wants to teach. The Quizzes, in-Lecture questions, Programming Exercises enable you to step through a path of- learning the theory, building the intuition, getting practical advices, implementing the code and inspiring you to work on your own projects. The Discussion Forum is always very active, you could clear any and every doubt of yours. Thanks and Congrats Prof. Andrew Ng on making the best MOOC ever!!

par Hooman R

•17 juin 2017

Excellent course. You will learn linear and logistic regression, SVM, neural networks and many more algorithms (supervised and unsupervised) You will also learn about how to evaluate the algorithms and how to design a more efficient system.

The teacher knows well how to teach the concepts in a way that you will gain a deep understanding rather than just memorizing formulas.

The quizzes are brilliantly designed to make sure you have learned the material entirely.

The computer assignments are created with profound details and you will do them in Octave or Matlab. Implementing the algorithms will help you fully understand what is happening in these algorithms. Enormous work has been done creating these assignments. Hats off to the designer of them.

The only thing that I would say could be better about this course, is the SVM topic. Unlike the other algorithms described in this course, the SVM algorithm has been explained in less detail than I expected (even with watching the optional videos). What I mean is, in order to gain a deep understanding of the SVM, one would need to see other sources as well. However, for any practical purposes, this algorithm has been explained well enough in this course.

par Chia-Yu C

•24 févr. 2020

Professor Ng definitely is gifted in the sense of turning complicated concepts into reachable, comprehendible ideas, which is the best thing I can expect when entering this complicated ML world.

To me, this class is more application-oriented rather than theory/math solid. Surely there are pros and the cons of doing that. One obvious advantage would be, even with limited math (mainly linear algebra and calculus) knowledge, ones can still have a great time playing with the ideas and models in ML field. The course definitely serves as a terrific introduction to this field. Andrew had all the ideas well-covered and made sure that after this class, students can apply these to real-world without bumping into some big troubles.

I don't think the lack of math proof or formula deduction can be fairly stated as the cons of this class. Though ones will definitely need to familiar those if they have serious ML jobs to be done, this class still serves as a great starting point to help people navigate which ideas/theories to dig deeper into.

To conclude, I highly recommend this class and encourage people who are looking for building more solid math foundations can find extra readings along the course :)

par Jeremy F

•8 août 2017

Excellent course. It has an easily understandable introduction and keeps gaining speed and complexity as you go on. While the first quizzes and assignments are quite easy, the later ones (except the final chapter) become a real challenge. I had to repeat some of them a few times. The additional resources are absolutely helpful, it's a shame I didn't use them until week 6 or 7.

I think by the end of the course everyone should understand what machine learning can do and what not, beautifully supported by real life examples. Until week 10 this course seriously left me wondering how machine learning is applied at all in a real-life work environment, but chapter 11 cleared things up.

The final chapter, as a whole, is the one where you know you've done the hard part. It's basically one big example to illustrate how you can apply your current knowledge on machine learning. It's your reward for all the patient learning.

You should still be aware that the world of machine learning is huge, and by learning the theory you merely scratch the surface of it if you complete this course. Me, for instance, I will move on to other courses to deepen my newly acquired skills, or to get some practical experience.

par KEVIN N

•8 févr. 2019

Exceptional. Andrew Ng brought a lot of himself in this class. He is a master of teaching complex topics in simple ways. I have learnt a lot from his teaching skill, in particularly on how to transform complex concepts into simple statements which is quite relevant in my job today. Not everyone is an engineer and yet many people around us have heard about ML. But many misconceptions are said. This class will help you make your message crystal clear. A big community has been growing up all those years and he deserves it.

I have started taking the class many years ago for free but had not the time to finish it because of a busy life as many of elearners here. Now it is done and I have paid for it without any regret. As a ML engineer, I had especially an eye into his ability to communicate complex concepts in simple way to his students. If you are a quantitative engineer, you may pass all 100% quite easily. But what matters here is not the hardness of the questions, it is all about listening to a talented teacher. I must say he masters the communication. He is an exceptional professor, a reference to teach online courses. I have taken many MOOCS for the past 5 years. This is easily a A++.

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