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
4710 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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Simon Collins profile image
Simon Collins profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 8 months 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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Gautam Sharma profile image
Gautam Sharma profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 9 months ago
The course is a "Life - Changer" ! Andrew Ng is the best professor I have ever had. He is the reason I have decided to pursue my masters in machine learning. He breaks a complex concept down into chunks which are simpler to understand and thereby explaining that concept. I feel like I can apply this knowledge to any domain, be it robotics, finance, biology, etc. Do go for this course, because it'll change your life. It has changed mine.
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Kristina Šekrst profile image
Kristina Šekrst profile image
10/10 starsCompleted
  • 102 reviews
  • 102 completed
3 years, 9 months ago
This is the best course on Coursera. I'm happy to see it become a self-paced class for everyone, but I believe that it should run regularly as well, since the course like this deserves to go live. The self-paced look of Coursera courses isn't as good as the live one, and this course focuses on Octave/Matlab programming assignments, which are a better fit to a live course. However, this is just a platform-choice critique, the course itself is simply amazing. However, watch out - it's not a beginner's course. Previous experience in linear algebra is strongly encouraged, and programming experience is required, otherwise you'll get stuck in the beginning. Huge recommendation!
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Kalpesh Patil profile image
Kalpesh Patil profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 9 months ago
One of the best ongoing online course on Machine Learning. Covered almost all aspects related to machine learning. I have studied Neural Networks during my masters but this course helped me a lot to understand basic concepts and other Machine Learning techniques.
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 profile image

10/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 10 months ago
A great course! The lecturer is very thorough, patient and encouraging. The in-video quizzes are challenging and very relevant for knowledge acquisition check, just like the programming assignments. It would be good if the course was even more encompassing, so it would cover Bayesian learning, decision trees, ensemble models, etc. Otherwise, an excellent choice for anyone into machine learning and data science.
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Student

10/10 starsCompleted
3 years, 11 months ago
This was an awesome course! I am a computer graphics software developer and my objective in taking it was to understand enough about machine learning to solve my own problems. The course exceeded my expectations. It is not terribly math-intensive so you can follow most of the material if you have some understanding of linear algebra. (As compared with the CalTech "Learning From Data" course which is more mathematical and actually goes thru proofs of the formulas that are just given to you in this course.) The exercises use MatLab (or Octave) but you are given enough information to solve them if you are not familiar with these languages. If you have been looking for an excuse to find out more about MatLab this course is perfect. If you want to know how to program neural nets in C++, you won't learn that here.
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Student

10/10 starsCompleted
4 years, 4 months ago
Excellent course to get your feet wet in machine learning. Andrew Ng is very clear in explaining the material and the programming assignments are interesting.
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Vichar Sanchar profile image
Vichar Sanchar profile image
10/10 starsCompleted
  • 5 reviews
  • 4 completed
4 years, 5 months ago
Coursera might not be as polished as EdX but it has some good courses. This one which is the grand daddy of all of those is no exception. Very engaging and interesting - even if you are not a programmer.
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Heonkyu Jin profile image
Heonkyu Jin profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 5 months ago
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Kristina Šekrst profile image
Kristina Šekrst profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 6 months ago
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Soroush Pour profile image
Soroush Pour profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
4 years, 6 months ago
Nothing but praise. Fantastic course that covered many fundamental aspects of machine learning with a good dose of theory and practical knowledge. Needs to be followed up with more courses on how to clean up and work with large datasets, but Professor Ng covered a huge amount in the time allocated. Would absolutely recommend this as a first course to anyone with a serious interest in machine learning that wants to learn it for practical use while still understanding many of the core theoretical concepts that make ML work in the real world (or not).
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Prashant Joshi profile image
Prashant Joshi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 9 months ago
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Antonio Muñoz profile image
Antonio Muñoz profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 10 months ago
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Andre Boechat profile image
Andre Boechat profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 10 months ago
The course allows us to understand very well the basics of many fields/applications of machine learning. In the end, the course contents became my main machine learning material to consult. PROS: * It's possible to learn very well a lot of topics of machine learning. * The math behind them is very well explained (more practically than theoretically). * It's easy to follow the class. The homeworks are interesting and well related to the lectures.] CONS: * If are a good programmer, the assignments are very easy. It is not that bad because we could focus on the most interesting part of the problem (from a machine learning perspective). Usually we have the desire to build everything from the scratch to know every details.
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Student profile image
Student profile image

Student

10/10 starsCompleted
4 years, 10 months ago
I had some prior experience in computer vision and wanted to deep my knowledge on the machine learning field. This class shown some of the main techniques, but what made a difference was the focus on how to decide what technique and how to evaluate it. Great professor and material. The class forum also was very helpful during the programing exercises.
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Muthu Jothi profile image
Muthu Jothi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 11 months ago
No Prior Experience. Best introductory course. Focus on concepts. The programming assignments are designed to be hand-holding type and prove to be a great means to understand the concepts of the lecture. The intuition that goes behind how to learn from data is well explained. Love this course and in fact it changed my view of how I look at data.
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hayim lusthaus profile image
hayim lusthaus profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 11 months ago
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Nicolas Pipard profile image
Nicolas Pipard profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 2 months ago
Excellent course! It really match my expectations (especially after reading all the reviews, I was hoping for something nice) I enjoyed every minutes of it. Actually, it was more hours… or even days, as I usually spent, every week, 1 work day on the course material, taking notes and review questions, then worked 1-2 evenings from home for the assignments (so a bit more than the 7 advertised hours, but I was trying to get the most out of it). It was quite intensive, but I learnt a huge amount that I will be able to apply to my current job. I also did get a good score (100%) so I feel confident that it is a good start for me… It was also extremely well organised (especially compared to a previous MOOC I have done), and the automatic correction worked marvelously.
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geo jsg profile image
geo jsg profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 2 months ago
No prior experience in machine learning. First time I discover it, just knew about what it was related generally but nothing else. Course is very well explained and easy to follow and progress. Duration is good and the only thing I could regret is not to have the continued course to explore more in depth machine learning.
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Kevin Zhu profile image
Kevin Zhu profile image
10/10 starsCompleted
  • 7 reviews
  • 5 completed
5 years, 2 months ago
This is a great course and with less math but with clear explanation. Programming assignments are also well-constructed.
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fayçal fatihi profile image
fayçal fatihi profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 2 months ago
i like in This course that will provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 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.
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Sharad Lal profile image
Sharad Lal profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 2 months ago
I have an engineering background but newly introduced to machine learning. Wonderfully structured and at a good pace. Manageable workload. The lectures, quiz and the programming assignments are clear and reinforce the concepts very well.
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Diego Rosado profile image
Diego Rosado profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
5 years, 4 months ago
This is the best course I've ever done. This guy is really awesome. I've learnt much more in a 15 minute video than in a course year in my hometown university. If you are interested in the field, this course is a must.
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Wei En profile image
Wei En profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
5 years, 5 months ago
This course was my first and I felt that professor Ng was able to explain everything very well with the use of graphs. This course is focused on implementing machine learning algorithms in the real world than the maths, which are left as optional algorithms. The programming assignments, while feeling like following detailed instructions, showed the power of machine learning in many ways (I won't spoil the assignments).
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Rajiv Abraham profile image
Rajiv Abraham profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 6 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 profile image
Student profile image

Student

10/10 starsCompleted
5 years, 6 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, 6 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 profile image
Student profile image

Student

10/10 starsCompleted
5 years, 6 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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胥嘉幸 profile image
胥嘉幸 profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 6 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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Melanie Mueller profile image
Melanie Mueller profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 5 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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  • 5 stars
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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.