Machine Learning

Provided by:
9/10 stars
based on  123 reviews
Provided by:
Cost FREE , Add a Verified Certificate for $79
Start Date Upcoming

Key Concepts

lightbulb
We've created a summary of key topics covered in this course to help you decide if it's the right one for you. Click individual badges to see more courses on the same topic.

Course Details

Cost

FREE,
Add a Verified Certificate for $79

Upcoming Schedule

  • Upcoming

Course Provider

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

Ratings details

  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

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.

Sort By
Student profile image
Student profile image

Student

10/10 starsCompleted
4 years, 7 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.
Was this review helpful? Yes0
 Flag
Vichar Sanchar profile image
Vichar Sanchar profile image
10/10 starsCompleted
  • 5 reviews
  • 4 completed
4 years, 8 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.
Was this review helpful? Yes0
 Flag
Heonkyu Jin profile image
Heonkyu Jin profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 8 months ago
Was this review helpful? Yes0
 Flag
Pablo Couto profile image
Pablo Couto profile image
9/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 9 months ago
Was this review helpful? Yes0
 Flag
Kristina Šekrst profile image
Kristina Šekrst profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 9 months ago
Was this review helpful? Yes0
 Flag
M Cloney profile image
M Cloney profile image
10/10 starsTaking Now
  • 3 reviews
  • 2 completed
4 years, 11 months 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!
Was this review helpful? Yes1
 Flag
Soroush Pour profile image
Soroush Pour profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
4 years, 9 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).
Was this review helpful? Yes0
 Flag
Shekhar Sivaraman profile image
Shekhar Sivaraman profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 10 months ago
This is a very basic introductory course to the field of machine Learning. i would have preferred for the examples and tests to have been in R with the use of libraries. It was unclear whether the intent was to learn matlab or to learn machine learning at times. All said and done, i am eagerly awaiting a follow-up more advanced course by Andrew Ng on the topic. I hope you are listening. :-)
Was this review helpful? Yes0
 Flag
Prashant Joshi profile image
Prashant Joshi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years ago
Was this review helpful? Yes0
 Flag
Emmanuel Ladoux profile image
Emmanuel Ladoux profile image
9/10 starsCompleted
  • 1 review
  • 1 completed
5 years ago
What I "disliked": \- Sometimes (slightly) lacks mathematical rigor \- The review questions are fairly (too) simple What I liked (much): \- The course is crystal clear, and spans a lot of topics \- The programming exercises are well designed and help mastering the topics I would highly recommend this course to anyone looking for an introduction on the matter.
Was this review helpful? Yes0
 Flag
Antonio Muñoz profile image
Antonio Muñoz profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 1 month ago
Was this review helpful? Yes0
 Flag
Andre Boechat profile image
Andre Boechat profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 1 month 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.
Was this review helpful? Yes0
 Flag
Student profile image
Student profile image

Student

10/10 starsCompleted
5 years, 1 month 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.
Was this review helpful? Yes0
 Flag
Greg Hamel profile image
Greg Hamel profile image
9/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 1 month ago
Machine Learning is one of the first programming MOOCs Coursera put online by Coursera founder Andrew Ng. Although Machine learning has run several times since its first offering and it doesn’t seem to have been changed or updated much since then, it holds up quite well. This course assumes that you have basic programming skills. Assignments also require many vector and matrix operations and slides include some long formulas expressed in summation notation so it is recommended to have some familiarity with linear algebra. You don't need to know calculus or statistics to take this course, but you may gain deeper insight into some of the material if you do. The course uses the Octave programming language, a free to use clone of MATLAB. The course runs 10 weeks and covers a variety of topics and algorithms in machine learning including gradient descent, linear and logistic regression, neural networks, support vector machines, clustering... Machine Learning is one of the first programming MOOCs Coursera put online by Coursera founder Andrew Ng. Although Machine learning has run several times since its first offering and it doesn’t seem to have been changed or updated much since then, it holds up quite well. This course assumes that you have basic programming skills. Assignments also require many vector and matrix operations and slides include some long formulas expressed in summation notation so it is recommended to have some familiarity with linear algebra. You don't need to know calculus or statistics to take this course, but you may gain deeper insight into some of the material if you do. The course uses the Octave programming language, a free to use clone of MATLAB. The course runs 10 weeks and covers a variety of topics and algorithms in machine learning including gradient descent, linear and logistic regression, neural networks, support vector machines, clustering, anomaly detection, recommender systems and general advice for applying machine learning techniques. Lectures are split into 3 to 15 minute segments with periodic quizzes and each topic section has a corresponding quiz. Section quizzes are worth 1/3 of the total grade but you get unlimited attempts (with a 10-minute retry timer.). Andrew Ng does a good job explaining dense material and slides although the audio levels are often too low. If you don' have good speakers you might need headphones to hear him talk. The other 2/3 of the course grade is based on 8 multi-part programming assignments that typically involve filling in code for key functions to implement machine learning algorithms covered in lecture. The course gives you a lot of structure and direction for each homework, so it is generally pretty clear what you are supposed to do and how you are supposed to do it even if you don't understand 100% of the materiel covered in lecture. Machine learning is a great course if you can get past quiet audio. If you've never used Octave or MATLAB before, don't let that stop you from taking this course; learning the basics necessary to do the assignments only takes a couple of hours and it will help you think of things in terms of vectorized operations.
Was this review helpful? Yes0
 Flag
Muthu Jothi profile image
Muthu Jothi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 2 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.
Was this review helpful? Yes0
 Flag
hayim lusthaus profile image
hayim lusthaus profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 2 months ago
Was this review helpful? Yes0
 Flag
Nicolas Pipard profile image
Nicolas Pipard profile image
10/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 5 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.
Was this review helpful? Yes0
 Flag
Richard Taylor profile image
Richard Taylor profile image
10/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 2 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.
Was this review helpful? Yes3
 Flag
Hamideh Iraj profile image
Hamideh Iraj profile image
10/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 6 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.
Was this review helpful? Yes1
 Flag
T. A. profile image
T. A. profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 4 months ago
Prior experience: whatever Sebastian Thrun introduced in his pre-Udacity AI course. This course taught a lot of machine learning techniques, and spent a good deal of time discussing when and why to use one or another. The course I took was taught in Octave, which was easy to pick up.
Was this review helpful? Yes0
 Flag
geo jsg profile image
geo jsg profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 5 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.
Was this review helpful? Yes0
 Flag
Kevin Zhu profile image
Kevin Zhu profile image
10/10 starsCompleted
  • 7 reviews
  • 5 completed
5 years, 5 months ago
This is a great course and with less math but with clear explanation. Programming assignments are also well-constructed.
Was this review helpful? Yes0
 Flag
Ivo Fernandes profile image
Ivo Fernandes profile image
2/10 starsDropped
  • 15 reviews
  • 9 completed
5 years, 5 months ago
I simply cant even understand a single word, the diction is too bad, I dont like to be focused in the subtitles, if I wanted it, I have picked a book :)
Was this review helpful? Yes0
 Flag
fayçal fatihi profile image
fayçal fatihi profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 5 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.
Was this review helpful? Yes0
 Flag
Sharad Lal profile image
Sharad Lal profile image
10/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 5 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.
Was this review helpful? Yes0
 Flag
Jeff Winchell profile image
Jeff Winchell profile image
7/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 8 months ago
This is not nearly as theoretical as the Cal Tech course, and the problems aren't as fun as the Berkley AI course, but it gives you a larger survey of techniques to apply to machine learning problems. Some of this material is quite complex. The programming exercises are simplified due to this, but some can still be quite challenging.
Was this review helpful? Yes3
 Flag
Dan profile image
Dan profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
5 years, 9 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).
Was this review helpful? Yes2
 Flag
dukebody profile image
dukebody profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 8 months ago
My background: former Artificial Intelligence master student. I took this course to refresh some concepts on Machine Learning (covered extensively in my master program) and ended up learning more. The instructor does very good at teaching the concepts in an easy way for any background level, with lots of detail. There is also a lot of work put in preparing the assignments. The only "but" I see to this course is that you are allowed to attempt the quizzes so many times and the programming assignments are sooo guided that (a) all students will have a very high grade, not differentiating at all and (b) you might end up learning a lot less because everything is almost done in the assignments.
Was this review helpful? Yes1
 Flag
Diego Rosado profile image
Diego Rosado profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
5 years, 7 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.
Was this review helpful? Yes0
 Flag
Wei En profile image
Wei En profile image
10/10 starsCompleted
  • 2 reviews
  • 1 completed
5 years, 8 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).
Was this review helpful? Yes0
 Flag

Rating Details


  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

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.