Machine Learning

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9/10 stars
based on  122 reviews
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Coursera online courses
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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
4454 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
122 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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P. Lepin profile image
P. Lepin profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 1 month ago
An excellent introduction into essential machine learning techniques. The course is very rich in content, and covers a lot of ground, but doesn't ever devolve into empty hand-waving. The course favours practical approach to machine learning, and will often skip the theory and/or underlying principles (leaving formula derivation as a purely optional exercise for those interested in this aspect of ML). Prof. Ng is obviously enthusiastic about the subject, and the course as a whole feels very polished. On the downside, the programming assignments are not very challenging and do not require any creativity, as they boil down to following very detailed instructions. The assignments remain quite instructive despite that, as there's a lot of support code meant to visualize the results and provide various statistics to help students understand how does everything work. This doesn't seem to be an oversight or anything like that, but rather con... An excellent introduction into essential machine learning techniques. The course is very rich in content, and covers a lot of ground, but doesn't ever devolve into empty hand-waving. The course favours practical approach to machine learning, and will often skip the theory and/or underlying principles (leaving formula derivation as a purely optional exercise for those interested in this aspect of ML). Prof. Ng is obviously enthusiastic about the subject, and the course as a whole feels very polished. On the downside, the programming assignments are not very challenging and do not require any creativity, as they boil down to following very detailed instructions. The assignments remain quite instructive despite that, as there's a lot of support code meant to visualize the results and provide various statistics to help students understand how does everything work. This doesn't seem to be an oversight or anything like that, but rather conscious course design as a 'ML cookbook'. Since going through this class last spring I actually employed a few of the techniques taught in my day-to-day work, and this class was instrumental in sparkling my newfound interest for statistics. Required skills: elementary algebra, coding skills Recommended skills: first-order logic, linear algebra, probability & statistics, multivariate calculus, Octave Workload: low Difficulty: low Value: high Fun: high
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Thomas Johnson profile image
Thomas Johnson profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years 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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Richard Taylor profile image
Richard Taylor profile image
10/10 starsCompleted
  • 29 reviews
  • 28 completed
3 years, 4 months ago
An excellent introduction to the world of machine learning. The way the material is presented is fanastic. The lessons are crafted to teach the student how the algorithms work and how you can code them along with the basic mathematical foundations. Other ML courses start with the math and are difficult to follow. Every time Andrew explains a topic you will think "I can do that" and you will in the programming assignments. Andrew Ng is probably the best instructor a course like this can have and his lessons are a treasure. Fantastic course in all aspects.
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Jewel Lambert profile image
Jewel Lambert profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 5 months ago
Excellent course. The videos were very clear and informative. The quizzes were short and to the point, serving as a useful adjunct to the videos - there was no attempt to make them needlessly tricky or to require rote memorization. If you understand the fundamental ideas from the lectures, the quizzes will simply reinforce that knowledge without being difficult or time consuming. The homework assignments were nicely structured. They are designed to require little programming or mathematics expertise, since everything is already laid out for you, and your tasks are simply to fill in a few lines of code here and there to ensure that you understand how things work. Yet at the same time, you can't complete the assignment _without_ understanding the key ideas. A perfect balance for an introductory course, and an approach that allows busy professionals to complete the course with a minimal time commitment (about 2-3 hours per week for lect... Excellent course. The videos were very clear and informative. The quizzes were short and to the point, serving as a useful adjunct to the videos - there was no attempt to make them needlessly tricky or to require rote memorization. If you understand the fundamental ideas from the lectures, the quizzes will simply reinforce that knowledge without being difficult or time consuming. The homework assignments were nicely structured. They are designed to require little programming or mathematics expertise, since everything is already laid out for you, and your tasks are simply to fill in a few lines of code here and there to ensure that you understand how things work. Yet at the same time, you can't complete the assignment _without_ understanding the key ideas. A perfect balance for an introductory course, and an approach that allows busy professionals to complete the course with a minimal time commitment (about 2-3 hours per week for lectures + quizzes + homework). All in all, one of the highest compliments I can give to the course is that it's easy. That's a very high compliment indeed for a subject whose theoretical underpinnings involve some pretty sophisticated differential equations, statistics, and linear algebra. Yet Andrew Ng pulls it off, all while providing a solid, practical foundation for further study. Only a few weeks into the course, and you've already learned enough to implement your own neural network-based OCR system from scratch... and you'll be surprised by how much easier it was than you thought it would be! Probably the best MOOC I've taken to date.
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Gavin Conran profile image
Gavin Conran profile image
10/10 starsCompleted
  • 25 reviews
  • 25 completed
5 years, 1 month 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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Swizec Teller profile image
Swizec Teller profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 2 months ago
Took this course when it first came out and I really loved it. Prof. Ng is possibly the best person I have ever listened to explain a complex topic. Was sitting a real life machine learning class at the same time and the two just cannot compare. The online class went through the material much quicker and focused more on things that are practical rather than things that were thought promising twenty years ago but have since fallen out of popular use.
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Dmitry Kozhedubov profile image
Dmitry Kozhedubov profile image
10/10 starsCompleted
  • 23 reviews
  • 15 completed
9 months, 3 weeks ago
Certain candidate for the MOOC Hall of Fame - one of the earliest and best courses. Great, highly theoretical, introductory course to key machine learning principles and algorithms. I agree with Henry Harya that it's best to redo the coursework in your language of choice, if fact, many students were organizing study groups to do just that. I think this course makes a great pairing with edX's The Analytics Edge to create a perfect balance of theory and practice - I suggest to take both if you're just starting in the field.
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Dan profile image
Dan profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 10 months ago
Great class, starting at very basic level (it even covers what matrixes are!) but still covering a lot and going enough in depth especially on some important topics like how to detect model overfitting. The class covers only a subset of the main machine learning methods used today, but it does that well and is an excellent starting point for the machine learning uninitiated. The programming assignments are the best ones I've ever seen on coursera so far, with all the tedious parts already taken care of for you, good hints on how to fill in the important functions. Good and clear explanations and visualizations.Programming assignments were in Octave (a sort of free Matlab clone) and learning it was a good skill to gain (although I also find the Windows version of Octave a bit frustrating due to many bugs).
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Robert Komartin profile image
Robert Komartin profile image
10/10 starsCompleted
  • 19 reviews
  • 16 completed
5 years, 1 month 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
5 years, 1 month 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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Greg van de Krol profile image
Greg van de Krol profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
1 year, 7 months ago
Wonderful introduction to Machine Learning. Andrew Ng takes you step by step through the processes and even without any prior experience or knowledge of MatLab, within 5 weeks you'll be building neural networks to recognize faces. Each project is extremely well organized and Andrew is great at explaining complex concepts and gives you great practical advice. I highly recommend his course.
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M Cloney profile image
M Cloney profile image
10/10 starsTaking Now
  • 3 reviews
  • 2 completed
3 years ago
This is one of the best MOOCs out there, folks. If you're interested in Machine Learning, have the tenacity to install and learn how to use Octave or Matlab, and have at least 10 hours a week to devote to watching lectures, doing quizzes and programming assignments, I can't recommend this highly enough. I'm sad that this class will be over for me in just a few short weeks!
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Hamideh Iraj profile image
Hamideh Iraj profile image
10/10 starsCompleted
  • 70 reviews
  • 60 completed
3 years, 8 months ago
It is the best course I have ever taken on MOOCs. You might not believe, but I enjoy it more than watching a movie. Because Dr Ng is excellent in his job. speaks very fluently, explains very simply so that you can completely make sense of the material and you have a good feeling inside. Programming assignments are really hard but if you can code everything from scratch in MATLAB or octave which is the goal of this course, you can fully understand what is going on, not being dependent on software programs to do it for you. I recomment it to everyone interested in machine learning. Even if you are not doing assignments, watch videos, learn and enjoy.
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John Y profile image
John Y profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
4 years, 6 months ago
I am half-way through this course, right now. I went in with very high expectations, because of this course's reputation, and the course is far better than I expected. I understand why some folks wish that the course would go deeper, but if it did, I probably would not be able to keep up, due to other commitments. So, from my perspective, it is perfect. I hope they make a Machine Learning II class!
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Sebastián Ramírez Montaño profile image
Sebastián Ramírez Montaño profile image
10/10 starsCompleted
  • 7 reviews
  • 7 completed
4 years, 12 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
4 years, 11 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
5 years 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
5 years, 1 month 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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10/10 starsCompleted
  • 1 review
  • 1 completed
4 months, 2 weeks ago
nice start into ML. good breadth of topics, which one realizes after checking out other courses later. will provide clarity on what topic to study in depth.
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Enald Green profile image
Enald Green profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
8 months, 4 weeks ago
Great course. The first course I took on website. It gave a good perspective on Machine learning. I appreciate all of it. This course is very fundamental and doesn't need some prerequisites. If you want to learn things about Machine Learning, this is the best course to start.
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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 55 reviews
  • 54 completed
11 months, 1 week ago
Of longstanding renown in the MOOC world, Stanford's machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning -- linear and logistic regression, neural networks, support vector machines, clustering, dimensionality reduction and principal component analysis, anomaly detection, and recommender systems. As with every other Stanford course I've taken, Prof. Ng precedes each segment with a motivating discussion and examples. Graded portions of the course include a quiz after every topic and a programming assignment, in MATLAB/Octave, after most of them. The programming assignments are excellent. Although 95% of the code comes to you pre-written, what you write really goes to the heart of that week's topics. Given the breadth of the course, these assignments nicely provide depth and meaningful rigor. The quizzes are very fair and sometimes nic... Of longstanding renown in the MOOC world, Stanford's machine learning course really is the definitive introduction to this topic. The course broadly covers all of the major areas of machine learning -- linear and logistic regression, neural networks, support vector machines, clustering, dimensionality reduction and principal component analysis, anomaly detection, and recommender systems. As with every other Stanford course I've taken, Prof. Ng precedes each segment with a motivating discussion and examples. Graded portions of the course include a quiz after every topic and a programming assignment, in MATLAB/Octave, after most of them. The programming assignments are excellent. Although 95% of the code comes to you pre-written, what you write really goes to the heart of that week's topics. Given the breadth of the course, these assignments nicely provide depth and meaningful rigor. The quizzes are very fair and sometimes nicely open your eyes to subtleties of the topic you may not have appreciated. Machine Learning has migrated along with all Coursera courses to their new platform, which offers the benefit of "on demand" scheduling flexibility (you can start whenever you want) but has some unfortunate downsides. Chief among these is the fact that the quizzes provide no feedback (as they used to) and can be taken as many times as you want. With enough persistence, anyone can score 100% in the course. These are minor deficiencies, however, and don't detract from this course's well-deserved reputation. Those who take and enjoy Machine Learning should consider following it up with The Analytics Edge, an MIT course offered through edX. The Analytics Edge is more about applying data analytics, including but not limited to machine learning techniques, to a wide variety of real-world problems. It's a great complement to this course, leading you through the many ways data can be parsed and processed to illuminate, predict and explain.
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Matt Herich profile image
Matt Herich profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
11 months, 2 weeks ago
This class is a great introduction for anyone interested in machine learning as it lays out the fundamentals in an easy to understand format. Andrew Ng is the chief scientist at Baidu and is well known in the fields of machine learning and artificial intelligence so you can rest assured you're learning from the best!
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10/10 starsCompleted
  • 1 review
  • 1 completed
11 months, 4 weeks ago
Great course to begin machine learning, using MATLAB archive assignment. Although it does't provide enough theory, it gives an intuition of machine learning. After this course you will be more comfortable to learning some deeper class in this area.
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Taras Petrytsyn profile image
Taras Petrytsyn profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
12 months ago
This course is very helpful as introduction to Machine Learning. Mr Ng did a great job! The best course on Coursera I took so far. I hope to see another, more deep course related to Machine Learning by Mr Ng.
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Student profile image

Student

10/10 starsCompleted
1 year, 4 months ago
This is the best online training experienced. The method of Coursera app on online training is amazing. It never felt like remote learning. A complex subjects is made easy by Prof Andrew. Many thanks for this course and all the effort by everyone involved including prof Andrew. Prof Andrew is the best!
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Sakares Saengkaew profile image
Sakares Saengkaew profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
1 year, 6 months ago
Excellent course for people who start in the machine learning field. It covers the necessary basics that you can continue to study by yourself in the future. Prof. Andrew explained the concept and workflow really well. I do highly recommend this course for any new ML starter.
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Bernardo Campos profile image
Bernardo Campos profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
1 year, 10 months ago
I took this course without previous knowledge about Machine Learning. I found it very interesting and motivating. The content is very useful and the progress in the topics is very well given. Along the course some mistakes appear, but they are corrected in the errata webpage.
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Gaurav Anand profile image
Gaurav Anand profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 11 months ago
This is a very good course for someone who has no prior knowledge in machine learning. The course is really hands on, you will get to internalize the material by doing the weekly assignments. Although the course doesn't require any pre-requisite knowledge but you should have good understanding of matrices in algebra to really understand the proofs.
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Karthikeyan Sankaran profile image
Karthikeyan Sankaran profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
1 year, 11 months ago
This was my first course in Machine Learning and am really glad to have taken this course to get introduced to ML. The instructor was excellent and inspirational. The best part to me was the intuition behind the algorithms. There was the right balance between mathematics, concepts and practical implementation. The programming exercises were interesting and at the right level of complexity. All in all, a great introductory course to Machine Learning and I will strongly recommend it to all ML / Data Science aspirants.
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Simon Collins profile image
Simon Collins profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
2 years ago
what an excellent course.... I'm definitely a lot further ahead in my understanding of machine learning techniques. Doing the assignments and quizzes also helped a lot
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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.