Machine Learning

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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.

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Humanities
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4699 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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Gui Ambros profile image
Gui Ambros profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 8 months ago
Excellent introductory course for Machine Learning newbies. Covers a lot of ground in just a couple of months, from regression & classification, to K-Means, SVM, Neural Networks and more. You'll be using GNU Octave (a free version of Matlab, but Matlab works as well if you have it). I did the first class (the one in '11, before Coursera was invented) and was amazed by the quality and professionalism of Prof. Andrew Ng. Not a surprise to see Coursera now growing so fast. We're definitely living the revolution of higher education.
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Ankur Sethi profile image
Ankur Sethi profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
6 years, 8 months ago
This is a great class. The professor does give excellent videos and the material is very practical.
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Jesse Spaulding profile image
Jesse Spaulding profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 1 month ago
I took this course in the Fall of 2011 and it's one of two courses that inspired me to create this site (CourseTalk). Here's the best things about this course: \- Andrew Ng is awesome. He's a top expert in the field and you really feel like he's your personal tutor. \- The course makes machine learning very easy to understand. \- High production value.
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Alex Parij profile image
Alex Parij profile image
9/10 starsCompleted
  • 2 reviews
  • 1 completed
6 years, 4 months ago
Overall the course was interesting. I wish the programming assignments were more engaging and not just to fill in couple of lines in Octave code.
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Pablo Couto profile image
Pablo Couto profile image
9/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 5 months ago
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Emmanuel Ladoux profile image
Emmanuel Ladoux profile image
9/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 8 months 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.
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Greg Hamel profile image
Greg Hamel profile image
9/10 starsCompleted
  • 116 reviews
  • 107 completed
4 years, 9 months 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.
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Student profile image

Student

9/10 starsTaking Now
5 years, 6 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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Student

9/10 starsCompleted
5 years, 11 months ago
Excellent course, great instructor! Pretty good coverage of machine learning methods, very good balance between maths and practice
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Student profile image

Student

9/10 starsCompleted
5 years, 11 months ago
An excellent introductory course. I had no background in this, but Andrew paces the course very well, especially in the first few weeks, covering subjects solidly and clearly. It also helped that, on occasion, he would show a complex formula, but say that it was not necessary to learn it, but rather for the interest of those more advanced in calculus. Took me a week to get my head thinking in matrices, but all makes sense now, and good to get a feel for a bunch of different machine learning algorithms from the inside out. Manually implementing them really forced me to get a handle on what the algorithm was actually doing.
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emediquei profile image
emediquei profile image
9/10 starsCompleted
  • 5 reviews
  • 5 completed
6 years, 4 months ago
A great teacher and interesting content. Some of the programming assignments don't help very much in understanding the topics, and they just require filling in some blanks in Octave. But a good course nevertheless.
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Afref Fetter profile image
Afref Fetter profile image
8/10 starsCompleted
  • 12 reviews
  • 12 completed
6 years, 6 months ago
Prior experience in the field: None Like: We are introduced to a wide array of topics from basic regression to SVMs. Practical applications of the techniques was shown in large-scale projects. We got to implement what we'd learned in the lectures through some excellent (and useful) programming assignments. Dislike: The course left me feeling I had only an "overview" of machine learning, rather than being able to say I'd learned the nitty-gritty details [This could be a good thing depending on what you want]. The quizzes didn't really test much. Templates provided for every programming assignment made this course quite a bit easier than it should have been. Suggested improvements: Discard the quizzes (or make them optional). Get 1 or 2 "heavy- duty" programming assignments - no templates, you start from scratch. Overall: Good as a machine learning course, but great as an introductory course.
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Henry Harya profile image
Henry Harya profile image
8/10 starsCompleted
  • 6 reviews
  • 5 completed
2 years, 5 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.
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dukebody profile image
dukebody profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
5 years, 4 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.
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Robin Sinclair profile image
Robin Sinclair profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 6 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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Karthik Puthraya profile image
Karthik Puthraya profile image
8/10 starsCompleted
  • 5 reviews
  • 5 completed
5 years, 10 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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Oles Tourko profile image
Oles Tourko profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
1 year, 10 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.
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Prashant Singh profile image
Prashant Singh profile image
8/10 starsTaking Now
  • 3 reviews
  • 1 completed
2 years, 2 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 :)
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Atheer Al Attar profile image
Atheer Al Attar profile image
8/10 starsCompleted
  • 3 reviews
  • 3 completed
2 years, 6 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.
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Student profile image
Student profile image

Student

8/10 starsCompleted
3 years, 2 months ago
I have to say that Andrew Ng is one of the best teacher I have ever had. He makes difficult subject very easy to understand. Content wise, I think it is a good introduction to machine learning algorithm.
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 profile image

8/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 9 months 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 :)
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Zbyněk Zajíc profile image
Zbyněk Zajíc profile image
8/10 starsCompleted
  • 18 reviews
  • 18 completed
3 years, 9 months 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.
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NOUGUIER Olivier profile image
NOUGUIER Olivier profile image
8/10 starsCompleted
  • 1 review
  • 1 completed
4 years 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.
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Michael Devereux profile image
Michael Devereux profile image
8/10 starsCompleted
  • 5 reviews
  • 4 completed
4 years, 1 month ago
Content: Covered a broad range of topics in modern day machine learning; but most importantly it started from a fairly simple concept - how to iteratively solve a derivative - that anyone having done higher level maths in high school would have been familiar with already. From there on it once you get that then it is just adding on various tweaks and related concepts. You get a focus in this course on the practical part of doing such analysis - that is the problems/pitfalls of each technique, what are its uses/misuses, how to "tune" the parameters of each technique to your liking etc. So all very useful for someone looking to actually implement this stuff, in real life! Instructor: Prof Ng is first and foremost an extremely good lecturer - I have never experienced US-style education but thanks to this course I badly wished I had done my undergrad in the US. Always breaking down difficult/annoying concepts to its basics, going... Content: Covered a broad range of topics in modern day machine learning; but most importantly it started from a fairly simple concept - how to iteratively solve a derivative - that anyone having done higher level maths in high school would have been familiar with already. From there on it once you get that then it is just adding on various tweaks and related concepts. You get a focus in this course on the practical part of doing such analysis - that is the problems/pitfalls of each technique, what are its uses/misuses, how to "tune" the parameters of each technique to your liking etc. So all very useful for someone looking to actually implement this stuff, in real life! Instructor: Prof Ng is first and foremost an extremely good lecturer - I have never experienced US-style education but thanks to this course I badly wished I had done my undergrad in the US. Always breaking down difficult/annoying concepts to its basics, going through carefully each equation - he is exactly who you might need to get through such a technical topic.
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Shekhar Sivaraman profile image
Shekhar Sivaraman profile image
8/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 6 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. :-)
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T. A. profile image
T. A. profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years 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.
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Maciej Pilichowski profile image
Maciej Pilichowski profile image
8/10 starsCompleted
  • 9 reviews
  • 9 completed
5 years, 7 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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Patrix Rembang profile image
Patrix Rembang profile image
8/10 starsCompleted
  • 10 reviews
  • 10 completed
6 years, 1 month ago
This course is a good introduction to Machine Learning. You will be exposed to a handful of supervised and unsupervised learning algorithm. The professor really did a good job explaining concepts without assuming his audience have background in calculus or linear algebra. The programming assignments are fun, but not really difficult. The downside of this course is the lack of math. If you're looking for hardcore or rigorous introduction to ML, you won't find it here. But if you just want to survey ML algorithms and some best practice advice, know some programming, and don't really know calculus and linear algebra, this is for you.
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Ethan Berl profile image
Ethan Berl profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
6 years, 7 months ago
This course is very good and well planned. Andrew Ng explains the material very well (albeit a bit slowly) and the content is extremely useful. The absolute most useful part of the course is that he focuses on how to tell when the algorithms are working and how to tell when something is going wrong. Over/under fitting, regularization, learning curves, precision vs. recall, etc. give a real insight into the subject rather than just handing the student a toolbox of algorithms which could be misused. The actual algorithms cover all the established techniques very well. The one big complaint I had with this course was that the homeworks and quizzes were too easy. You were able to fill in the few lines of Octave code without really having to understand the algorithm completely, which to me is a fatal flaw and defeats the purpose of the homework. I was able to get full points on everything but I know that I would not be able to implement S... This course is very good and well planned. Andrew Ng explains the material very well (albeit a bit slowly) and the content is extremely useful. The absolute most useful part of the course is that he focuses on how to tell when the algorithms are working and how to tell when something is going wrong. Over/under fitting, regularization, learning curves, precision vs. recall, etc. give a real insight into the subject rather than just handing the student a toolbox of algorithms which could be misused. The actual algorithms cover all the established techniques very well. The one big complaint I had with this course was that the homeworks and quizzes were too easy. You were able to fill in the few lines of Octave code without really having to understand the algorithm completely, which to me is a fatal flaw and defeats the purpose of the homework. I was able to get full points on everything but I know that I would not be able to implement SVM in another language after the course -- even though I do have a reasonable overview understanding of what the algorithm achieves. Because of this hole, I can't give the course a perfect rating but other than this, the video lectures were excellent and the material is so useful I often refer back to it even though the course ended several months ago.
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Jeff Winchell profile image
Jeff Winchell profile image
7/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 4 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.
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