Machine Learning

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9/10 stars
based on  123 reviews
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4721 reviews

Course Description

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... 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.
Reviews 9/10 stars
123 Reviews for Machine Learning

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Darya Prokurat profile image
Darya Prokurat profile image
10/10 starsCompleted
  • 8 reviews
  • 6 completed
5 years, 8 months ago
I can't say how easy it is without prior training - I already had some courses in university both - in machine learning and matlab. It is very useful for me to renew knowledge. (BS in Computer Science)
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Ivan Vashchenko profile image
Ivan Vashchenko profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 10 months ago
This was the first MOOC I have taken and it set a very high bar for others. Andrew Ng is a passionate lecturer and the material is structured very well. I highly recommend this course to anyone interested in AI.
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Student profile image
Student profile image

Student

10/10 starsTaking Now
5 years, 10 months ago
I am in week 5 of this 10-week course. I have had no previous experience with this subject and do not know other courses alike. I have no background in maths or computer science so it has been challenging. I like this course because it has guided me effectively into machine learning. It is very practical. Maths are just given and not demonstrated which can be a good thing because you can do that part in a maths course. Whoever that does not need that part can just go with the practical things. Exercises are shown step by step, which might be boring to people with background in computer science or alike but for us who do not, it is really helpful and still challenging. The videos, the quizzes, the exercises and the forums for peer discussion are very well implemented, everything works quite nicely. I am very grateful that this is available for free to anyone. Sincerely, Juan Ignacio Mendoza.
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Rafael Santos profile image
Rafael Santos profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 11 months ago
I had no experience with Machine Learning before. This was my first time in touch with the topic and I'm still impressed by the amount of things I learned and programmed myself just with that course. Prof Andrew is very didactic and knows how to pass the knowledge forward. So far, the best course I took on Coursera
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viashino profile image
viashino profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
5 years, 11 months ago
I took the first edition of this course, without having prior experience in Machine Learning. The explanations are clear and to the point. The assignments are simple since they are based on templates that allow you to focus on implementing the relevant parts only. Of course, if you want a more challenging experience, you can try to solve them from scratch. One of the best courses on coursera.
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Miki Tebeka profile image
Miki Tebeka profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
5 years, 11 months ago
Great instructor, explains things clearly. Apart from being a great instructor Andrew Ng is also a lead researcher in Machine Learning and has a ton of practical advices.
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Trevor profile image
Trevor profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years ago
A very logical progress that made complex machine learning concepts very easy to understand. A must do course for anyone interested in Machine Learning.
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Connie profile image
Connie profile image
10/10 starsCompleted
  • 10 reviews
  • 9 completed
6 years, 1 month ago
Awesome course. Excellent instructor. Inspiring me to pursue Introduction to Recommender Systems at Coursera.
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Student profile image
Student profile image

Student

10/10 starsCompleted
6 years, 2 months ago
this course was so awesome because its not depend on your background and it is present the ML to you with an interactive way , so yes its counts as one of the well-organized and perfect . Of course I like it so much and many many great thanks to Professor Andrew
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Marjan Ček profile image
Marjan Ček profile image
10/10 starsCompleted
  • 20 reviews
  • 17 completed
6 years, 2 months ago
The course is a really good introduction to Machine Learning with practical work and very well prepared expositions. Highly recommended.
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Brijrajsinh Gohil profile image
Brijrajsinh Gohil profile image
10/10 starsCompleted
  • 6 reviews
  • 6 completed
6 years, 2 months ago
This is great course. But for me a guy who's just completed 12th grade it's tough for me ! But I tried my best and completed the quizzes and assignments. But nice understanding!
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Student profile image
Student profile image

Student

10/10 starsCompleted
6 years, 2 months ago
I have no experience with this subject befor. It's easier to understand compare to others. I like the way the teacher used to teach us.
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Student profile image
Student profile image

Student

10/10 starsCompleted
6 years, 2 months ago
This is one of the best MOOCs I've taken (and I have taken many). I recommend it to anyone with a little programming experience and an interest in artificial intelligence/ statistics/ data analysis, etc. Machine Learning is one of the hottest topics in computer science today, and this course is an excellent introduction to some of the main ideas and techniques. It provides relatively easy, but interesting and practical, programming exercises that illustrate both the principles and the actual steps. Therse can be done in a reasonable amount of time even by someone like me with little programming experience. Andrew Ng has a rare ability to explain complicated ideas in a simple and intuitive way. Although I have a strong mathematical background, I applaud his efforts to reach those with weak backgrounds. If he had attempted to give rigorous mathematical explanations, he could not have covered much practical material. Students can find m... This is one of the best MOOCs I've taken (and I have taken many). I recommend it to anyone with a little programming experience and an interest in artificial intelligence/ statistics/ data analysis, etc. Machine Learning is one of the hottest topics in computer science today, and this course is an excellent introduction to some of the main ideas and techniques. It provides relatively easy, but interesting and practical, programming exercises that illustrate both the principles and the actual steps. Therse can be done in a reasonable amount of time even by someone like me with little programming experience. Andrew Ng has a rare ability to explain complicated ideas in a simple and intuitive way. Although I have a strong mathematical background, I applaud his efforts to reach those with weak backgrounds. If he had attempted to give rigorous mathematical explanations, he could not have covered much practical material. Students can find much of this elsewhere. Ng's lectures on unsupervised learning are masterful in their clarity. The unit on the use of the Octave programming language stands by itself as an excellent introduction for students (like me) unfamiliar with Matlab. Not everything is perfect; the ideas behind neural networks remained foggy to me. After many decades of teaching technical subjects at the university level, I now take the point of view of the student. That is, I want to get the best learning experience for the least amount of time spent. For this standard alone I give this course five stars!
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Student profile image
Student profile image

Student

10/10 starsCompleted
6 years, 3 months ago
I had no prior experience with this subject but after doing this course my interest in machine learning goes at the top. It is really nice course taken by professor Andrew Ng!! I like the part how beautifully professor Ng design the course and the materials (real life application data) collected from different sources makes this course very attractive. He made us understand most of the algorithm very easily through different angle such as sometimes showing video clips, giving real life example. The problem exercises and review questions was resourceful and help us to fathom the link between the video lectures and our depth of learning.
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Esteban Afonso profile image
Esteban Afonso profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 3 months ago
This class is a practical introduction to machine learning. Dr. Ng's lectures are clear and well-organized and he does an excellent job of conveying the intuition behind these machine learning algorithms and how to implement them effectively, while also making their technical aspects digestible. The work in the course consists of quizzes and MATLAB programming assignments. The programming assignments often felt more like exercises in linear algebra than machine learning (though I suppose applying linear algebra is a useful skill for doing machine learning). The main value of the programming assignments was getting to see these machine learning algorithms in action, as well as providing you with, essentially, a MATLAB machine learning algorithm recipe book to play around with on your own applications. Great introduction to machine learning, well-organized and well-delivered, very practical. Highly recommend.
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Nikhil sarpotdar profile image
Nikhil sarpotdar profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 4 months ago
I missed the first two sessions of the course because I didn't know what Machine Learning was and figures it was some theoretical topic with little practical application. Then over the last few months I have been reading and hearing a lot about Machine Learning and how it is being used everywhere and hence I decided to take it. This course is fairly mathematical but the math is easily accessible. But Prof Ng's lectures are amazingly clear, precise and easy to follow. The homeworks allowed us to solve real practical problems that the machine learning community at large solves. I used the Neural Network we learned in the class for digit recognition to enter the kaggle competition. This class made a lot of cutting edge (and not necessarily easy to understand concepts) fairly easily accessible. It is easy to see that Prof. Ng really knows this stuff. It is obvious that there has been a LOT of work that has gone in preparation for this cl... I missed the first two sessions of the course because I didn't know what Machine Learning was and figures it was some theoretical topic with little practical application. Then over the last few months I have been reading and hearing a lot about Machine Learning and how it is being used everywhere and hence I decided to take it. This course is fairly mathematical but the math is easily accessible. But Prof Ng's lectures are amazingly clear, precise and easy to follow. The homeworks allowed us to solve real practical problems that the machine learning community at large solves. I used the Neural Network we learned in the class for digit recognition to enter the kaggle competition. This class made a lot of cutting edge (and not necessarily easy to understand concepts) fairly easily accessible. It is easy to see that Prof. Ng really knows this stuff. It is obvious that there has been a LOT of work that has gone in preparation for this class and I am very happy to have taken it.
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Jasper Brener profile image
Jasper Brener profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 4 months ago
My 40+ years of experience as a university professor stimulated my interest in Coursera and led me to enroll in the Machine Language course. From the outset I was thoroughly engaged and inspired by what I experienced. Professor Ng is an excellent teacher who uses the online medium to maximum effect. The lectures, quizzes, exercises and discussion forums were arranged to provide a coherent learning environment that kept me thoroughly involved and interested. I learned a lot in the course and after completing it, I was convinced that the Coursera project, which is freely available to anyone who has access to a computer and an internet connection, provides a realistic way of raising the educational level of the planet.
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Duncan Murray profile image
Duncan Murray profile image
10/10 starsCompleted
  • 25 reviews
  • 24 completed
6 years, 2 months ago
Almost finished this, but so far this is the best course I have taken - it is really well done and Andrew is a great teacher. You end up with practical skills on machine learning, and although the maths looks quite complex he takes the time to explain it well. This is also the only MOOC I have come across which has: \- complete wiki pages, well laid out with all key facts \- in video quizzes which are actually timed correctly (why do some courses have questions about things they haven't talked about yet?) \- well designed assignments. I like the template idea, as that is not unlike what happens in the real world \- great feedback system on the assignments (at each stage they build in tests that you can check to see how you are going before you have to submit the assignment) I would absolutely recommend this course to anyone interested in the subject, as although there is a lot of maths, you will end up with practical skills at the en... Almost finished this, but so far this is the best course I have taken - it is really well done and Andrew is a great teacher. You end up with practical skills on machine learning, and although the maths looks quite complex he takes the time to explain it well. This is also the only MOOC I have come across which has: \- complete wiki pages, well laid out with all key facts \- in video quizzes which are actually timed correctly (why do some courses have questions about things they haven't talked about yet?) \- well designed assignments. I like the template idea, as that is not unlike what happens in the real world \- great feedback system on the assignments (at each stage they build in tests that you can check to see how you are going before you have to submit the assignment) I would absolutely recommend this course to anyone interested in the subject, as although there is a lot of maths, you will end up with practical skills at the end (and Octave is pretty impressive tool to learn)
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Chris Beard profile image
Chris Beard profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
6 years, 5 months ago
Can't say enough good things about this course and prof. Probably single most impactful course in deciding my career.
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Ilya Krukov profile image
Ilya Krukov profile image
10/10 starsCompleted
  • 6 reviews
  • 6 completed
4 years, 4 months ago
Great course from Coursera's father. I can recommend this course to everyone who wanted to start learning ML discipline. Lecturer ( BTW one of the best scientists in ML area ) is very passionate about topic. He has a talent to explain complicated things in very gentle an easy manner. This course is invaluable introduction to ML topic, and must be taken before more advanced courses like "Natural Language Processing" or "Neural Networks for Machine Learning".
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Jon Gauthier profile image
Jon Gauthier profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 6 months ago
\-- What was your prior experience in the field? None. (Web developer since 2006.) \-- What did you learn? The Stanford ML course teaches a set of immediately applicable machine learning algorithms, from linear regression to feedforward neural networks. Professor Ng consistently includes in his lectures notes on the implementation of the content presented. He is straightforward about the caveats of the methods described in the course, and spends an entire section of the course enumerating the various ways to diagnose which errors are affecting a given implementation / application and how to make the proper correction. \-- Did the course meet expectations? The course easily exceeded my expectations. The concepts in this course now serve as an entire new set of utilities on my toolbelt as a computer programmer. They have been enormously useful and have without a doubt added to my value as a programmer. \-- What didn't you like? The mos... \-- What was your prior experience in the field? None. (Web developer since 2006.) \-- What did you learn? The Stanford ML course teaches a set of immediately applicable machine learning algorithms, from linear regression to feedforward neural networks. Professor Ng consistently includes in his lectures notes on the implementation of the content presented. He is straightforward about the caveats of the methods described in the course, and spends an entire section of the course enumerating the various ways to diagnose which errors are affecting a given implementation / application and how to make the proper correction. \-- Did the course meet expectations? The course easily exceeded my expectations. The concepts in this course now serve as an entire new set of utilities on my toolbelt as a computer programmer. They have been enormously useful and have without a doubt added to my value as a programmer. \-- What didn't you like? The most difficult math that was fully covered in the course dealt with matrix algebra. Concepts with steps involving calculus or linear algebra were only briefly described. While an understanding of the mathematical underpinnings is not required to build a competent implementation of one of the ML algorithms taught, it would have been interesting to see more (potentially optional) lectures on the more technical mathematical support that ML depends upon.
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xasmx profile image
xasmx profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
6 years, 7 months ago
An excellent introductory course to machine learning. It's heavily focused on practical issues of machine learning and after it you'll be able to use machine learning for your own purposes. The course does not cover theory of machine learning, so if your interest is more in the theory than in practice, you might feel that the course doesn't go deep enough. I prefer to first learn the practice and after that go deeper into the theoretical aspects, so this was the perfect introductory course for me.
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Mayank Singh profile image
Mayank Singh profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
6 years, 9 months ago
A course that interested me in going for higher studies in this area. Couldn't believe I had created a handwriting recognizer. OCR was another awesome project. The projects weren't too tough but good enough for a beginner. The course is best for beginners wanting to experience the world of ML. I disagree with people about the course being watered down. It's meant for newbies and not an advanced course on the subject. Don't forget to check out kaggle when you're done with the course.
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Taqi profile image
Taqi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 10 months ago
The best course I have taken in my life ! This Dr is awesome ! The programming material is great and everyone can follow. Make sure to have enough time to complete the homework.
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Luka Kacil profile image
Luka Kacil profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 11 months ago
I really enjoyed Ng's course. He's one of the few professors who are also really awesome lecturers. And the course itself is very interesting.
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Alex Ott profile image
Alex Ott profile image
10/10 starsCompleted
  • 6 reviews
  • 5 completed
6 years, 11 months ago
I took initial version of this course, when Coursera wasn't founded yet. I want to say, that I really liked this course - lectures & additional materials completely covered everything what I need to make it complete. Andrew Ng has ability to explain complex things in very simple language, and although this course isn't so complex from mathematical point of view, it gave me enough background to start to dig deeper, into mathematical basics of ML and related stuff. Home works were very well designed.
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Chris Simmons profile image
Chris Simmons profile image
10/10 starsCompleted
  • 7 reviews
  • 7 completed
6 years, 12 months ago
This is the course that got me interested in MOOC's - it deserves 5 stars just for that, but it was a great course as well. I took the initial offering of this course in Fall 2011, before Coursera existed. The material is interesting, covering a broad range of machine learning approaches. The programming assignments are reasonable if you have a computer science background, and would be much easier if you have experience with a data-based language like Octave, R, Matlab, etc. One nit, and this is minor. Several times throughout the course, Andrew mentioned that learning the material presented in the course would put you above most ML users in Silicon Valley. Now that I'm in a company that does machine learning at a very large scale (albeit not located in the Valley), I find this assessment a bit questionable - these people really know their stuff. Overall, a great course, and increasingly important in the era of big data.
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Marek Stój profile image
Marek Stój profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 12 months ago
Very pragmatic approach to machine learning, the professor has great teaching skills. Programming assignments are extensive and very fun to complete.
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Ricardo Teixeira profile image
Ricardo Teixeira profile image
10/10 starsCompleted
  • 86 reviews
  • 77 completed
6 years, 12 months ago
This was the best MOOC I took to date. Professor Ng has amazing teaching skills, particularly because he teaches such a hard class. The length of the lessons is just right, and the material he prepares for programming assignments is great because if guides you through the exercise. Overall, a tremendous experience whether you have previous programming experience or not.
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Ben Haley profile image
Ben Haley profile image
10/10 starsCompleted
  • 5 reviews
  • 4 completed
7 years ago
Simply the best MOOC I have taken.
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