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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Rajiv Abraham profile image
Rajiv Abraham profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 9 months ago
I knew nothing about ML before I joined. For other courses, I 'audited' on the course of Big Data and Web Intelligence and partially completed the algorithms course on Udacity. This course was great. I learned a lot about ML. For a programmer, this may slightly stretch you but it is not difficult. You learn a new language called Octave. I always recommend learning new languages suited to different problem domains(matrices in this case). The community was strong and helpful so that really helps when you don't know what is going on with your code
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Student

10/10 starsCompleted
5 years, 9 months ago
I had a phenomenal time taking this course with Professor Ng. He explains things very well, and in an easy to understand format. The discussion forums are constantly active which will prove very beneficial if you need to ask questions, as well as get "checked up" on by answering other questions. Overall, it is a great course! The only thing is, I honestly feel some things could be explained in more detail. Very seldom, however, still occurring, there are topics that are not explained in as deep as detail as other topics and some of them only superficial. This wasn't a major obstacle though.
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Juanli Shen profile image
Juanli Shen profile image
10/10 starsCompleted
  • 3 reviews
  • 2 completed
5 years, 9 months ago
Great course! The teacher has the ability to make hard things easy to understand. I never encounter any math problem through this class. The labs are easy to complete but if you have interest in how the whole system is running you can dig into the details of the given material. It's impressive how powerful Matlab is.
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Student

10/10 starsCompleted
5 years, 9 months ago
Excellent course!! My prior experience was in college when I got BS degree of Computer Information Systems I had to work hard to finish... but was rewarding!!! I could never imagine that I would learn so much from a online FREE course. Professor Andrew Ng took care to fit the course in a MOOC format. There are several short videos (5~20min) each week instead of one big video. I really liked this format. The programming exercises are challenging, but fun. The quizzes for each week are short (5 questions). Just made to you verify if you understood the material. I would recommend this course to anyone... you only need a little experience programming (any language).
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Robin Sinclair profile image
Robin Sinclair profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 10 months ago
Contents: What this course is not is a general summary of machine learning techniques. Instead it is an in-depth course on a number of the commonly used numerical techniques such as Multivariate Linear Regression ( use for predicting values), Logistic Regression ( for classification problems ) and mathematically modelled neural networks. Presentation: The Lectures are generally well presented but are of a highly mathematical nature. Resources: The practical side of the course is based on Octave, a free variant of Matlab. Coursework: The coursework consists of machine marked review questions an a series of practical problems to be solved Octave. Starter code is supplied for the practicals – they are marked by the server. Summary: This is a course for professional with a good mathematical background. If you are likely to need the techniques then is is worth doing the course. It is not for people who are merely curious.
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胥嘉幸 profile image
胥嘉幸 profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 9 months ago
Highly recommend.Although some mathematical concepts may just silghtly mentioned and skipped in this class,and it is better for u to using wiki and google to extend them.So andrew made it easily for u to have a entire view of machine learning introduction.It works efficiently to me.
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Student

3/10 starsCompleted
5 years, 10 months ago
The course material was excellent. The teaching delved into some areas but there was a fair amount of "see the lecture notes". The course emphasizes proofs and derivations. It is a notational orgy.
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Melanie Mueller profile image
Melanie Mueller profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 8 months ago
Very pedagogical and well though-through class on machine learning. Focuses on giving you the practical version of the most common machine learning algorithms - it is excellent for machine learning beginners who want to get a feel for the subject, or who want to quickly try a standard algorithm in their own work. The lectures mostly skip theoretical underpinnings and don't go deep. Lectures place a lot of emphasis on intuitively understanding what's going on. The review questions and assignments very closely follow the course material and are not very challenging. They test whether you have followed and understood the lectures, but don't challenge you to fiddle around until you have figured out the solution to a problem yourself.
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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

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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Student

9/10 starsTaking Now
5 years, 10 months ago
For those who are practically minded and like to see the big picture this is a really good course. Professor Ng is clearly a very smart guy but to his credit he can also "teach down". However, this course will be difficult to follow if you have no programming (esp Matlab/Octave) experience.
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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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Maciej Pilichowski profile image
Maciej Pilichowski profile image
8/10 starsCompleted
  • 9 reviews
  • 9 completed
5 years, 11 months ago
I love the enthusiasm of Prof.Ng and the quality of the lectures. You simply cannot not understand what you see and hear. However this is theoretical part -- the course includes homeworks, but they are so easy ("fill the gaps" kind) that they are barely useful. In other words -- you won't get a chance to get your hands dirty during this course.
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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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Karthik Puthraya profile image
Karthik Puthraya profile image
8/10 starsCompleted
  • 5 reviews
  • 5 completed
6 years, 2 months ago
No prior experience in the core subject matter. I did have experience in Matlab programming and knew a fair bit of probability and statistics. The good: Excellent survey of existing ML techniques. The course is peppered with very useful tips and tricks to real-life scenarios. The assignments are thorough and Prof. Ng's enthusiasm for the subject is very evident. The class forums were also very lively and added to the overall experience of the course. The bad: The assignments are too watered-down and usually involve the student adding <10 lines of code to complete the assignments. All the heavy- work programming is already done and given as a template. This is my only major complaint about the course. The major emphasis of the course was to get the student to start using ML techniques without spending much time to understand the fundamentals. In fact, I think this aspect was heavily emphasized. Unfortunately, not all of have a day-job... No prior experience in the core subject matter. I did have experience in Matlab programming and knew a fair bit of probability and statistics. The good: Excellent survey of existing ML techniques. The course is peppered with very useful tips and tricks to real-life scenarios. The assignments are thorough and Prof. Ng's enthusiasm for the subject is very evident. The class forums were also very lively and added to the overall experience of the course. The bad: The assignments are too watered-down and usually involve the student adding <10 lines of code to complete the assignments. All the heavy- work programming is already done and given as a template. This is my only major complaint about the course. The major emphasis of the course was to get the student to start using ML techniques without spending much time to understand the fundamentals. In fact, I think this aspect was heavily emphasized. Unfortunately, not all of have a day-job where we use ML to solve everyday problems. Someone like me who likes the mathematical rigour will be a little disappointed. To compensate for this, I did the full-fledged version of the Stanford course along with this where Andrew goes through all the math which is skipped in the Coursera version. All in all, a very good intro course and a MOOC done right.
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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

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

10/10 starsCompleted
6 years, 3 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

10/10 starsCompleted
6 years, 3 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

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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Jewel Lambert profile image
Jewel Lambert profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
6 years, 3 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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Equanimous Creativity profile image
Equanimous Creativity profile image
7/10 starsCompleted
  • 33 reviews
  • 32 completed
6 years, 3 months ago
I was a little disappointed with this course, It might be that I had high expeditions because I heard a lot of good about it. Don't misunderstand me this course is a very good first course on machine learning. The lectures, review questions and programming assignments is very good and easy. But the material have been simplified and details is missing compared to the Stanford lectures. A subject I find very useful when working with learning algorithm's is information theory but it was not mentioned with one word. A good course if you have never heard about linear/logistic regressions, neural networks and clustering but If you have seen it before there is not much inside and details to get in this course.
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John Y profile image
John Y profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
6 years, 4 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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Student

9/10 starsCompleted
6 years, 3 months ago
Excellent course, great instructor! Pretty good coverage of machine learning methods, very good balance between maths and practice
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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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