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.

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Humanities
Sciences & Technology
4720 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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9/10 starsCompleted
6 years, 3 months ago
An excellent introductory course. I had no background in this, but Andrew paces the course very well, especially in the first few weeks, covering subjects solidly and clearly. It also helped that, on occasion, he would show a complex formula, but say that it was not necessary to learn it, but rather for the interest of those more advanced in calculus. Took me a week to get my head thinking in matrices, but all makes sense now, and good to get a feel for a bunch of different machine learning algorithms from the inside out. Manually implementing them really forced me to get a handle on what the algorithm was actually doing.
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Nikhil sarpotdar profile image
Nikhil sarpotdar profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 3 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, 3 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, 4 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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Alex Parij profile image
Alex Parij profile image
9/10 starsCompleted
  • 2 reviews
  • 1 completed
6 years, 8 months ago
Overall the course was interesting. I wish the programming assignments were more engaging and not just to fill in couple of lines in Octave code.
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Jon Gauthier profile image
Jon Gauthier profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 5 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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emediquei profile image
emediquei profile image
9/10 starsCompleted
  • 5 reviews
  • 5 completed
6 years, 7 months ago
A great teacher and interesting content. Some of the programming assignments don't help very much in understanding the topics, and they just require filling in some blanks in Octave. But a good course nevertheless.
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Mayank Singh profile image
Mayank Singh profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
6 years, 8 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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Afref Fetter profile image
Afref Fetter profile image
8/10 starsCompleted
  • 12 reviews
  • 12 completed
6 years, 9 months ago
Prior experience in the field: None Like: We are introduced to a wide array of topics from basic regression to SVMs. Practical applications of the techniques was shown in large-scale projects. We got to implement what we'd learned in the lectures through some excellent (and useful) programming assignments. Dislike: The course left me feeling I had only an "overview" of machine learning, rather than being able to say I'd learned the nitty-gritty details [This could be a good thing depending on what you want]. The quizzes didn't really test much. Templates provided for every programming assignment made this course quite a bit easier than it should have been. Suggested improvements: Discard the quizzes (or make them optional). Get 1 or 2 "heavy- duty" programming assignments - no templates, you start from scratch. Overall: Good as a machine learning course, but great as an introductory course.
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Thomas Johnson profile image
Thomas Johnson profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
6 years, 9 months ago
One of the best classes I've taken. Ng provides excellent intuition for the algorithms he covers. The practical advice on designing machine learning pipelines in the latter part of the course is perhaps the best part. One downside is that some important machine learning techniques like decision trees and ensemble methods are not covered - I would love to see a "Machine Learning II" course by Ng
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Gavin Conran profile image
Gavin Conran profile image
10/10 starsCompleted
  • 25 reviews
  • 25 completed
6 years, 11 months ago
This was the first MOOC I took and have completed a number of them since. Some have been wonderful but Andrew's ML course still reigns supreme.
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Robert Komartin profile image
Robert Komartin profile image
10/10 starsCompleted
  • 19 reviews
  • 16 completed
6 years, 11 months ago
Excellent! I definitely recommend it!
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Ruslan Bes profile image
Ruslan Bes profile image
10/10 starsCompleted
  • 9 reviews
  • 8 completed
6 years, 11 months ago
Prerequisites: some programming background would be good Programming Exercises: maybe even too easy, almost everything is explained in detail. Video Lectures: there are three main chapters — Linear/Logistic Regression, Neural Networks and several extensions and applications of these concepts in learning algorithms. The lectures usually have too extensive explanations so I watched most of the videos on speed 1.25. What I've learned from the course: \- Algorithms of how to transform a lot of raw data into the meaningful statistics that allows to make a decision. \- Writing software that recognizes hand-written symbols isn't that hard. Same thing about recommender systems and spam-filtering. \- Side-effect: When one have to deal with arrays of data sometimes there is a better solution than writing a loop (vectorization). \- Side-effect: How to use Octave for simple mathematical tasks.
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Sebastián Ramírez Montaño profile image
Sebastián Ramírez Montaño profile image
10/10 starsCompleted
  • 7 reviews
  • 7 completed
6 years, 9 months ago
One of the greatest! Professor Andrew Ng is great, he makes you understand and doesn't try to make you feel dumb, he explains it all that you need to use Machine Learning without overwhelming you with mathematical complexities. This course has many of the greatest Machine Learning algorithms that you can use to work in many many applications.
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Panagiotis profile image
Panagiotis profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 8 months ago
Great introductory course! The discussion forum is really useful.. The programming assignments are meaningful and to the point..
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Daniel Snider profile image
Daniel Snider profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 9 months ago
I loved tis class so much. It was brilliantly taught which made for an extremely enjoyable learning process. This course makes the Coursera system rock.
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Sergio Marchesini profile image
Sergio Marchesini profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 10 months ago
excellent course, great teaching, learning material was perfect. Andrew is an excellent teacher, plenty of code examples and real world applications...
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Taqi profile image
Taqi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 9 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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Patrix Rembang profile image
Patrix Rembang profile image
8/10 starsCompleted
  • 10 reviews
  • 10 completed
6 years, 5 months ago
This course is a good introduction to Machine Learning. You will be exposed to a handful of supervised and unsupervised learning algorithm. The professor really did a good job explaining concepts without assuming his audience have background in calculus or linear algebra. The programming assignments are fun, but not really difficult. The downside of this course is the lack of math. If you're looking for hardcore or rigorous introduction to ML, you won't find it here. But if you just want to survey ML algorithms and some best practice advice, know some programming, and don't really know calculus and linear algebra, this is for you.
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Ethan Berl profile image
Ethan Berl profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 10 months ago
This course is very good and well planned. Andrew Ng explains the material very well (albeit a bit slowly) and the content is extremely useful. The absolute most useful part of the course is that he focuses on how to tell when the algorithms are working and how to tell when something is going wrong. Over/under fitting, regularization, learning curves, precision vs. recall, etc. give a real insight into the subject rather than just handing the student a toolbox of algorithms which could be misused. The actual algorithms cover all the established techniques very well. The one big complaint I had with this course was that the homeworks and quizzes were too easy. You were able to fill in the few lines of Octave code without really having to understand the algorithm completely, which to me is a fatal flaw and defeats the purpose of the homework. I was able to get full points on everything but I know that I would not be able to implement S... This course is very good and well planned. Andrew Ng explains the material very well (albeit a bit slowly) and the content is extremely useful. The absolute most useful part of the course is that he focuses on how to tell when the algorithms are working and how to tell when something is going wrong. Over/under fitting, regularization, learning curves, precision vs. recall, etc. give a real insight into the subject rather than just handing the student a toolbox of algorithms which could be misused. The actual algorithms cover all the established techniques very well. The one big complaint I had with this course was that the homeworks and quizzes were too easy. You were able to fill in the few lines of Octave code without really having to understand the algorithm completely, which to me is a fatal flaw and defeats the purpose of the homework. I was able to get full points on everything but I know that I would not be able to implement SVM in another language after the course -- even though I do have a reasonable overview understanding of what the algorithm achieves. Because of this hole, I can't give the course a perfect rating but other than this, the video lectures were excellent and the material is so useful I often refer back to it even though the course ended several months ago.
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Luka Kacil profile image
Luka Kacil profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 10 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, 10 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, 11 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, 11 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, 11 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
6 years, 11 months ago
Simply the best MOOC I have taken.
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Gui Ambros profile image
Gui Ambros profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 11 months ago
Excellent introductory course for Machine Learning newbies. Covers a lot of ground in just a couple of months, from regression & classification, to K-Means, SVM, Neural Networks and more. You'll be using GNU Octave (a free version of Matlab, but Matlab works as well if you have it). I did the first class (the one in '11, before Coursera was invented) and was amazed by the quality and professionalism of Prof. Andrew Ng. Not a surprise to see Coursera now growing so fast. We're definitely living the revolution of higher education.
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