Machine Learning

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9/10 stars
based on  16 reviews
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edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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Sciences & Technology
Business & Management
22042 reviews

Course Description

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major perspectives covered include:

  • probabilistic versus non-probabilistic modeling
  • supervised versus unsupervised learning

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course we will cover supervised learning techniques for regression and classification. I...

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major perspectives covered include:

  • probabilistic versus non-probabilistic modeling
  • supervised versus unsupervised learning

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.

In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Machine Learning course image
Reviews 9/10 stars
16 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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Deans Charbal profile image
Deans Charbal profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 7 months ago
This is my second course about Machine Learning (I'm a senior developer with a mathematical background) and I am delighted. This course is probably the best online ML course you can find. Professor John Paisley is an amazing and brilliant teacher ; he doesn't only have the knowledge, he has the talent of teaching, of transmitting knowledge with detail and precision, of explaining, reexplaining, illustrating sometimes complex concepts to make them simpler to understand and digest. I would like to thank him warmly, because my linear algebra and math background were pretty rusted, but it was a so-interesting 3 months, challenging for some parts but at the end of the day, it open perspectives, it provides extra tools of knowledge, it gives you keys of understanding. What a pleasure. About the content itself, some may think the content is very advanced or complex, while people more familiar with the topic may argue it's just an introdu... This is my second course about Machine Learning (I'm a senior developer with a mathematical background) and I am delighted. This course is probably the best online ML course you can find. Professor John Paisley is an amazing and brilliant teacher ; he doesn't only have the knowledge, he has the talent of teaching, of transmitting knowledge with detail and precision, of explaining, reexplaining, illustrating sometimes complex concepts to make them simpler to understand and digest. I would like to thank him warmly, because my linear algebra and math background were pretty rusted, but it was a so-interesting 3 months, challenging for some parts but at the end of the day, it open perspectives, it provides extra tools of knowledge, it gives you keys of understanding. What a pleasure. About the content itself, some may think the content is very advanced or complex, while people more familiar with the topic may argue it's just an introduction. Like an other person reviewing the course, I would warn people that it's heavy on mathematics (linear algebra, calculus). If you're familiar with it, just move forward and take this course. Last thing, lots of people are complaining about Vocareum. I didn't have any problem with it. Just have to wait that your files are saved before submitting them to grading (my first submission was empty because of that, the problem never happened again when I cared about it).
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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 59 reviews
  • 57 completed
11 months, 3 weeks ago
Buckle up -- this deep, mathematically rigorous dive into the major areas of machine learning is fast-paced and challenging. In fact, most of the course is less about machine learning than the math behind it -- problem-solving and applications take a back seat to the underlying mathematical techniques. You won't see very many implementation examples or, in the programming projects, watch a machine get smarter. For that context, you either need prior ML training or to have taken the first course in the AI MicroMaster series, Artificial Intelligence with Ansaf Salleb-Aouissi, which treats most of the topics in this course at a higher, more introductory level. Also, considerable fluency in probability and statistics is assumed -- at the level of MITx 6.041, for example. Although no textbook is suggested, I found "The Elements of Statistical Learning" by Hastie et al. to be quite useful. The topics covered are numerous, too many to... Buckle up -- this deep, mathematically rigorous dive into the major areas of machine learning is fast-paced and challenging. In fact, most of the course is less about machine learning than the math behind it -- problem-solving and applications take a back seat to the underlying mathematical techniques. You won't see very many implementation examples or, in the programming projects, watch a machine get smarter. For that context, you either need prior ML training or to have taken the first course in the AI MicroMaster series, Artificial Intelligence with Ansaf Salleb-Aouissi, which treats most of the topics in this course at a higher, more introductory level. Also, considerable fluency in probability and statistics is assumed -- at the level of MITx 6.041, for example. Although no textbook is suggested, I found "The Elements of Statistical Learning" by Hastie et al. to be quite useful. The topics covered are numerous, too many to list without putting you to sleep, but they span all of the common machine learning techniques except neural networks, which is a subject in itself (and is nicely covered at a high level in the AI course). There are four programming projects, weekly quizzes, and a final exam that counts for a whopping 45% of your grade. Prof. Paisley's lectures are clear and deftly unpack difficult mathematical principles and derivations, although he speaks a bit slowly and I found that playing the lectures at 1.25x speed delivered a more natural cadence. All in all this is a great course, but be forewarned.
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Yogesh Luthra profile image
Yogesh Luthra profile image

Yogesh Luthra

10/10 starsTaking Now
1 year, 5 months ago
This is the most insightful course I cam across. Although I am a trained practitioner of ML concepts, but there were some topics like Gaussian Processes, Collaborative Filtering, LDA etc, for which I need more satisfactory explanations. This course not only served that purpose but instructor beautifully explains all the concepts in sufficient detail!
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Henry Harya profile image
Henry Harya profile image
10/10 starsCompleted
  • 6 reviews
  • 5 completed
1 year, 8 months ago
The materials are mathematically rigorous and really provide insight on how to analyse, design and evaluate learning algorithms. Prof Paisley lectures are dense, though unfortunately he's not the greatest lecturer and spends most of the time reading from the slides. As a student you must be passionate about the material and mathematics to appreciate this course.
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Supat Ratanasirivilai profile image
Supat Ratanasirivilai profile image

Supat Ratanasirivilai

10/10 starsCompleted
1 year, 9 months ago
I am brand new in this field and would like to explore how it can be used in my work. I am also new in python programming but experienced in programming in VBA. With this two facts, I still found the lectures very rich in substance, to the point, covering ample topics in relatively short amount of time. Prof. Paisley excellently described difficult concepts clearly and concisely, though sometimes hard to grasp because of the difficulty of the subject itself. The four projects were good starting points for translating the concepts we learned from the lectures into real programming in python. The course was very difficult mathematically and conceptually in the beginning but half way through the course, I found it easier getting used to the concepts of machine learning. And being new in python, I had to spend longer hours to completes the projects writing programs in python and at the same time dealing with incompatibility between vers... I am brand new in this field and would like to explore how it can be used in my work. I am also new in python programming but experienced in programming in VBA. With this two facts, I still found the lectures very rich in substance, to the point, covering ample topics in relatively short amount of time. Prof. Paisley excellently described difficult concepts clearly and concisely, though sometimes hard to grasp because of the difficulty of the subject itself. The four projects were good starting points for translating the concepts we learned from the lectures into real programming in python. The course was very difficult mathematically and conceptually in the beginning but half way through the course, I found it easier getting used to the concepts of machine learning. And being new in python, I had to spend longer hours to completes the projects writing programs in python and at the same time dealing with incompatibility between versions of python used in Vocareum (the course grader) and the version 3.6 which I used. Nevertheless, I found the experience a very rewarding one. I just have two suggestions: 1) Vocareum should be modified to accept python 3.6 so beginners in python like me would not have suffered finding bugs which did not exist in python 3.6 but showed up in python 2.7 used by Vocareum during grading. 2) We have learned many great concepts and theories after finishing this course. It would be perfect to have a follow up course like "Case Studies in Applying Machine Learning." This will give a great chance for us to sharpen our knowledge to apply to real world problems. My last words: Excellent course and thank you for creating such a great course for the learning community.
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student

9/10 starsCompleted
1 year, 10 months ago
The course provides a solid theoretical introduction in Machine Learning. The concepts are efficiently presented through the video lectures by Prof. Paisley and there are 4 programming assignments to implement some of the algorithms. The course is somewhat demanding for the average learner and requires to invest time, but I found the overall learning experience really rewarding.
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Nick Burnett profile image
Nick Burnett profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 11 months ago
Course was a great overview of the theory behind a wide range of models. The theory gives a good intuition about what the models are doing and how best to make use of them. My effectiveness has improved, even using models I thought I was very familiar with. The lecturer was excellent and did a good job of communicating difficult concepts well. The course isn't easy. You need a strong mathematical background and a decent amount of time to get through it properly. Many of the students who had done other similar courses commented it was at a much higher level than most. The course is far more theoretical than practical, which for me was perfect as I get the practical side in my work . There was no info on things like categorical variables, dummies or dealing with outliers for example. There are projects though they are usually done on generated data. Thanks very much to EDX and Prof. Paisley.
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Student

10/10 starsCompleted
1 year, 11 months ago
There are not many courses online that provide such in-depth learning experience in Machine Learning. This course goes into some details and mathematics of the algorithms being used. It demands a good amount of time every week to understand and apply all that is being taught but that is what makes it good. It is not like many other courses that you can take and pass with minimal effort but at the end of it, it is worth spending time taking this course.
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Matthew Mcivor profile image
Matthew Mcivor profile image

Matthew Mcivor

7/10 starsCompleted
1 year, 11 months ago
Great Course! Quite theoretical but easy to grasp. For anyone who is interested in AI from a more ‘business point of view’ I got myself online certified at AIcompany.co in order to understand how ai can be integrated in my line of business. I would say it is great way to understand how ai works without going to technical. Understanding ai basics is (nowadays) a must and great way to have distinctive resume. In our organization it is now mandatory for all stakeholders of ai solution teams to be certified. I prefer Aicompany.co over other platforms since it is more applicable from a strategic / management level point of view.
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Student profile image

Student

10/10 starsCompleted
1 year, 12 months ago
No doubt, I learned most fundamental concepts in Machine Learning. The presentation and author are just perfect. The submission of assignments thtough Vocareum was disaster and could be accomplished while the code was running with no single errors. Staff from Columbia must work harder to overcome this issue, specially in assignments No. 3 and No. 4. The final test and proctor were again disaster. I could not take it, because I have latest Hybrid processor laptop (HP computer). ColumbiaX must fix this issue and make it more friendly even for iPhone and iPod users. BR
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Angel Bravo profile image
Angel Bravo profile image

Angel Bravo

10/10 starsCompleted
1 year, 12 months ago
I enjoyed the global view of machine learning that Prof. John Paisley gave in a magistral way. The course has a good balance between theory and practical work. The projects need some time to be completed, but they are the means to learn the usual tools in machine learning. I am very satisfied with the learning experience I had in this course.
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Kalok Kam profile image
Kalok Kam profile image

Kalok Kam

8/10 starsCompleted
1 year, 12 months ago
The course covers a wide range of machine learning techniques. The teacher is nice and his teaching is clear and clever. The programming assignments are full of fun and I have learned a lot from them. But the setting for assignments are somehow poor because most questions only accept one attempt and none answer is shown once you failed. I cannot learn from my mistake so finally I still feel unconfident about some topics.
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Ruben Gonzalez profile image
Ruben Gonzalez profile image

Ruben Gonzalez

10/10 starsTaking Now
2 years, 1 month ago
Over all my years of student I've come across with professors who aren't brilliant, with professors who are brilliant but they don't know how to explain the stuff clearly and with professors who are brilliant and know how explain the stuff clearly. Pr.Paisley belongs to the third group. As the description says, it's and advanced course, so you better look carfully at the prerequisites. The only "negative" aspect, in my oppinion, is that the staff is not very active in the discussion forums.
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Xiaoliang Zhou profile image
Xiaoliang Zhou profile image

Xiaoliang Zhou

10/10 starsTaking Now
2 years, 1 month ago
Dr. Paisley and his supervisor are the best in the area of Bayesian models of machine learning. Both of them have been students of Michael Jordan, the father of machine learning, and both are teaching in Columbia. I've selected his Bayesian models of machine learning last semester and is doing his machine learning here in Columbia. He is the best teacher of machine learning among all ML professors in Columbia for his ability to explaining stuff clearly. Up to 240 students have selected his course this semester, the largest number among all professors giving machine learning in Columbia. Thanks to Dr. Paisley.
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student

10/10 starsTaking Now
2 years, 2 months ago
This is a great course and I'm really liking it so far. The instructor's language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too. If I would have a small complaint -- the delivery can be bit dry in places. Perhaps its difficult to deliver a lively lecture when you're the only one speaking. However, I would still give the instructor full marks for explaining the material extremely well. The quizes are not very difficult. In addition to quizes there are some projects but none have been posted so far. I assume the projects will be where some of the heavy lifting in the course will be. The lecture material is tough but this is intended to be a master's level course. If you listen to the lectures carefully and do the suggested readings there will be a lot of rewards. Yes, you need a decent calculus/linear algebra background to take this course ... This is a great course and I'm really liking it so far. The instructor's language is precise and that is, to my mind, one of the strongest points of the course. The lectures are of high quality and the slides are great too. If I would have a small complaint -- the delivery can be bit dry in places. Perhaps its difficult to deliver a lively lecture when you're the only one speaking. However, I would still give the instructor full marks for explaining the material extremely well. The quizes are not very difficult. In addition to quizes there are some projects but none have been posted so far. I assume the projects will be where some of the heavy lifting in the course will be. The lecture material is tough but this is intended to be a master's level course. If you listen to the lectures carefully and do the suggested readings there will be a lot of rewards. Yes, you need a decent calculus/linear algebra background to take this course but I think its necessary to do justice to the the subject matter.
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John Langley profile image
John Langley profile image

John Langley

10/10 starsDropped
2 years, 2 months ago
The course is very interesting but nearly impossible to complete without strong mathematical background. It's not a simple "master degree" level course, it's a "math master degree" level course. For me master of molecular biology it's too difficult.
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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.