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Coursera online courses
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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
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.
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- 1 review
- 1 completed
9 years 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
- 2 reviews
- 2 completed
7 months, 3 weeks ago
An excellent course on Machine Learning with a rigorous mathematical approach and auto-corrected programming exercises! A great experience I couldn't recommend it more!
- 1 review
- 1 completed
2 years, 11 months ago
An introductory course that doesn't go into too much depth but covers a fair amount of material and is challenging enough. Didactically very well done.
- 2 reviews
- 2 completed
4 years, 2 months ago
This course is an approachable introduction to machine learning. It gives you tools you can immediately use for practical applications.
You should know basic linear algebra (matrix multiplication, transposes, dot products, etc...) and what derivatives are, and be comfortable with mathematical notation. You won't be required to do something like differentiate a function, and in this sense the course isn't very mathematically rigorous. The assignments are educational but on the easier side - implement the rest of some algorithm, mostly.
Perhaps this is the best route for an introductory level course though, and its a really good (and fun) introduction. I think you'll get the most value from it by applying it to your own projects.
Also, check out Stanford’s official version of the course (CS 229) for a more complete approach. Its also taught by Andrew, and the slides and videos are available freely.
- 23 reviews
- 15 completed
4 years, 8 months 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.
- 6 reviews
- 5 completed
4 years, 9 months ago
Great introduction, though not enough practical work. Most of the programming is done in Octave, which is limiting. I found my self taking the coursework and redoing it in Python to better understand it.
- 4 reviews
- 3 completed
4 years, 7 months ago
-I am doing programming assignment of week 2.
-Knowing linear regression(statistics) and polynomial interpolation will help you very much.
-I found here that we a software equivalent to Matlab ,which is Octave and it is open source,free and light weight :)
- 3 reviews
- 3 completed
4 years, 7 months 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.
- 59 reviews
- 57 completed
4 years, 9 months 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.
- 1 review
- 1 completed
4 years, 10 months 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!
- 1 review
- 1 completed
4 years, 10 months 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.
- 1 review
- 1 completed
4 years, 10 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.
- 3 reviews
- 3 completed
4 years, 10 months ago
Great course, Dr. Andrew takes baby steps approach to advance in the course. What I really like about the course is it's greatly organized and the mentors will have all your questions answered. In addition to that all the previous courses of this course were used as lesson learned, so you will find a long forum of errat, best practice and tutorial that 85% will have your issue solved before you think about using the forums. One thing to the new learners, this course requires a bit of time commitment so keep that in mind. I am planning to continue the learning and I am taking Neural Networks for Machine Learning now from the Univ. of Toronto.
Student
10/10 starsCompleted
5 years, 2 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!
- 4 reviews
- 4 completed
5 years, 5 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.
- 2 reviews
- 2 completed
5 years, 5 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.
- 2 reviews
- 2 completed
5 years, 8 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.
- 1 review
- 1 completed
5 years, 9 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.
- 4 reviews
- 4 completed
5 years, 9 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.
- 1 review
- 1 completed
5 years, 11 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
- 1 review
- 1 completed
6 years 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.
- 102 reviews
- 102 completed
6 years 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!
- 1 review
- 1 completed
6 years 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.
- 1 review
- 1 completed
6 years, 1 month ago
Its a good course for someone that never learned Machine Learning (like me :)). The workload is pretty small: about 2-3 Hrs a week (if you complete the course in 11 weeks), and you get exposed to a lot of ideas and algorithms in the field. I really liked the fact that programming assignments where presenting real problems that seems not trivial at all, in example: character recognition and image compression.
On the downside - the programming assignments requires very little thinking and give only a taste of the topic, and not a practical ability. overall - I enjoyed this course :)
- 1 review
- 1 completed
6 years, 1 month 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.
- 18 reviews
- 18 completed
6 years, 1 month ago
Very intuitive introduction to the basics of Machine Learning. Fully recommended for beginners in this field or for reminders. This was my first course.
Student
10/10 starsCompleted
6 years, 2 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.
- 1 review
- 1 completed
6 years, 5 months ago
Instructor was very clear. Exercices were challenging enough to illustrate the courses. IMHO, the only thing that was missing was to use a "real" programming language to drive learning.