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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....

Jan 25, 2020

Perfect foundational overview of the topic with challenging exercises, at least for someone who left university over 20 years ago and has since then not done much with his skills in Linear Algebra ;-)

Oct 31, 2017

Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.

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von Adam P

•Feb 02, 2019

I found this class to be excellent. Professor Ng presents the materials in an approachable way that goes into the mathematical intuitions of the topics. As always further mathematical study (in this case specifically linear algebra) would be helpful, but for the length and scope of the course there is an excellent balance of mathematics with practical usage. I also found myself agreeing with the notion that Matlab was useful as a prototyping tool (a view I didn't initially hold), and I use it for that on my own now when the situation warrants it. I personally would've liked an overview of the tools for these types of algorithms in any of a number of programming languages, but I can see how that is not really the focus of the course, and with the fundamentals gained from this course transitioning to other tools should just be a matter of familiarity with the tools, rather than the algorithms they implement.

von Barnaby R

•Nov 05, 2015

This is a great course ! The pacing is just right. Andrew covers each topic in very easily digestible bits and is very careful to not overwhelm beginners. I find this technique incredibly refreshing and open and welcoming. It is all too easy for professors to act like elites in their ivory towers and make you feel foolish for not knowing enough but Andrew is the opposite of that and actively encourages you to push through difficult topics by focusing on the high level details first and leaving it up to you to research the details on your own.

The programming exercises are set up so that all the distracting details are all coded already and it's up to you to fill in only the parts that are directly relevant to the topic you are learning. There's only one exercise that is slightly confusing but it's easy to get help on it in the forums if there's a helpful moderator like Tom Mosher around !

Highly recommended.

von Arnaud L

•Feb 10, 2016

Excellent course, perfectly paced and detailed. The professor Andrew Ng succeeds in creating a comfortable atmosphere and is easy to follow.

As a beginner in machine learning, I appreciated the fact that the contents cover the most important aspects of machine learning, without diving in too fast in the underlying mathematics and computational details. I think I need go deeper in these aspects now, but these lessons are rich of practical and methodological tips that i'm sure even advanced engineers would need.

Last but not least, the Octave/Matlab hands-on exercises permit a deeper understanding of the algorithms, and to realize what remained misunderstood after the lectures.

I am happy that I decided to start (and to end) this course. This was my first on Coursera : there will be others. Many thanks to Mr Ng, to the mentors that helped us on the discussion boards, and to the coursera team in general.

Arnaud.

von Marnie L

•Mar 21, 2019

Professor Andrew Ng's Machine Learning course was an excellent learning experience. The course was well organized and pedagogically sophisticated. The combination of hands on exercises, quizzes, and lectures made the course enjoyable, engaging, and easy to learn and retain. I believe that Professor Seymour Papert would approve of the methods in this course. The exercises provided the students with the opportunity to interact with the material. By providing a framework of well designed code, with the task of writing the core concepts on our own, students could focus on the important things and simultaneously learn from the expert and optimized code provided. We were expected to use vectorized implementations, which enabled us to both think about the problem in a mathematical way and write code that is efficient and scalable. This course is a Machine Learning classic. Thank you Professor Ng and tutors!!

von SAROJ S

•Sep 19, 2019

First let me thank Professor Ang for all the effort he had put in. My over reaching objective was to get

knowledge about Machine Learning which I read about all the time. I was very happy that I could put to use all the knowledge I had learned years back in linear algebra and statistics . I find that I am motivated to learn more and use it in my business consultancy ventures and build data products for our clients and I will make my younger staff to learn areas of Machine Learning, Neural Networks, Deep Learning and Data Analytics and build their futures accordingly.

Though this course is a one of the first in this field and many newer have been created in the recent past it allowed me to understand the basics rather than using “pre build” libraries and the functions given in them without really knowing the reasoning.

Thanks again to all the mentors who spend their valuable time helping the students.

von Dan N

•Aug 14, 2015

A fantastic hands-on introduction to machine learning. A very good balance of theory and math with the hands-on aspect. The math, when it goes into calculus, is there for the interested student but is optional.

This course is only introductory and should not be expected to provide anything approaching mastery in machine learning. Such mastery presupposes much deeper math skills than those covered here, along with much greater mastery of whatever language/tool is being used, and deeper knowledge and understanding of many other topics that are either merely touched upon here or skipped entirely. But you have to start somewhere, and this is a very good place to start.

A few of the later modules have some very distressing errors in the material that can cause enormous confusion. Otherwise the material is very solid and well-vetted.

The instructor is engaging and lively and his enthusiasm is infectious.

von Abdur R K

•Nov 15, 2017

Incredible course, introduces so many machine learning concepts flawlessly and leaves you excited for more, Andrew Ng is an amazing teacher and it's been said before, but I will say it again, he definitely has a knack for explaining complicated concepts in an easy to digest way. I completed the course in about six weeks (if anybody's wondering, because I know I was), in those six weeks it was about 26 full days of work with assignments and everything (yes I kept track), but realistically you should spread out the information and absorb it over time so that it sticks long term. I already have an engineering background (electrical engineering to be specific) and had taken courses on Linear Algebra, Statistics and Probability Theory so the math he points out seemed familiar, but if you're looking for an introduction to machine learning and the various terms associated with it, this course is for you!

von David N Y L

•Sep 18, 2017

The course provided me a very good concept of Machine Learning. The practice exercise are very good intellectual training for the understanding of the course materials. However, one must put some effort into it in order to get in-depth understanding of the materials, searching on the web for extra info for help to clarify the concept is necessary. Dr. Andrew Ng is good lecturer but a bit shy. However, learning with him is a pleasant experience. Because after this course, I found other similar ML course only repeat the very very basic concept with no details of how is done like that (just use the formula etc.). But this course is only focus on machine learning other that that; like cleaning data, remove unnecessary parameter, deal with missing value etc. are not the purpose of this course. I sincerely recommend this course for those who would like to know the "WHY" to participate in this course.

von BAPPADITYA D

•Sep 07, 2017

This is the course through which I started my journey with machine learning. I am really grateful to the Machine Learning community and specially Prof. Ng for making this course and the curricular available to so many students including me. I learned a lot from this course and beside this course helps me to think and dive into the deep of ML. Though I completed this course and got my certificate but no doubt I am surely going to miss this course. But I already started my journey with Prof. Ng once again with his deep learning course. Once again, I am thankful to the Coursera community, the Machine Learning community, the discussion forum members and last but not the least Prof. Ng for their tremendous effort, support and providing us the platform to learn and implement the crux of Machine Learning. A recommended course for researcher, self-learning student, industry personnel related with AI.

von C S

•Aug 30, 2017

This is an exceptionally good course where I learnt the fundamentals of machine learning in detail. The coding assignments "concretely" helped me commit these concepts to memory, give one a hands on feel for problem solving and it's just awesome to see your own work. The course is a little rough at the start. Once the coding assignments become more image based though it becomes easier to understand and visualise things.

The course in my opinion requires some linera algebra background. It is very time consuming, not only is 12 weeks long but coding assignments rarely take 3 hours as stipulated.

The course can be refered back to when looking for information about machine learning as it is well organised and contains high level problem solving info. I feel that I have a better understanding of what coursera was built for and where online learnings place is in the world after completing this course.

von Glen D

•Sep 17, 2018

Dr. Ng is an incredible instructor! The lectures meticulously lay out the math required and the instructor provides encouraging comments along the way. I was not looking forward to the math in this course but ultimately is was not only straightforward, but addicting! I started working a little ahead, then farther and farther ahead. I actually started wanting to spend all my free time on this, doing all the optional work, and taking the example code and expanding upon it for my own experiments. And now I have finished the course in about a month, and I am genuinely sad it is over. Why couldn't I have been this motivated about my classes when I was a university student years ago? Probably because Dr. Ng wasn't there! Great course. Excellent instructor. Lots of resource materials when you need them. Ex4 is a bit difficult and time-consuming but all the rest were very fast to complete.

von Sumit K

•Aug 18, 2019

I will never be able to thank Sir Andrew Ng and team Coursera enough for this opportunity. Sir Andrew Ng is truly a genius. I have learned a lot here not just technical skills but also way of thinking and approaching problems in general. I feel like growing up as a person here, and that's how machine learning also works. Isn't it? I'm a bioinformatics student, so I needed this skill. I've already started thinking of on how I can apply these algorithms on biological data to solve problems in genetics. Lastly, I'm convinced by others that this is the best course on machine learning. I'm not in a state or position to review all the efforts you guys have put to make such complicated looking course so simple. For those who are new to machine learning and thinking of doing this course, please do it without any hesitation. Blessings to all of you, and I wish you all luck for your future projects.

von DL

•Jun 17, 2019

Excellent introductory course on Machine Learning, even for people who don't know a whole lot of linear algebra. One can do this course in two ways: First is to look through all the pre-existing code in the test exercises and really understand how all it works and the second is to only focus on the portions that the course expects you to finish. Just finishing this course would not make you an "expert", but this is a very well structured course to get basic concepts firmly rooted in-place. Since this way my first foray, and I am not a good programmer, I was looking to focus on concepts, which this course truly enabled.

Finally, any good course is as good as the teacher makes it to be. Andrew has done an excellent job in taking the most important aspects and simplifying them into bite size pieces. My gratitude to him for the excellent work he has done for the benefit of learners at large.

von Marcel D S

•Jan 29, 2019

Absolutely fantastic course. Professor Ng's approach to teaching is a bit more top-down, so you'll get a lot of useful theory and intuition about how the different Machien Learning algorithms presented in this course actually work. Do not expect to be terribly fluent in implementing these algorithms after completing this course, though. I found that the programming exercises really show their age and although I recognize that Matlab/Octave is a nice programming language to implement matrix operations, I still feel that having to use this language is the weakest part of this course. But there is no single course that will teach you everything you need to know about Machine Learning. Considering the huge amount of intuition I've gained, I feel like this "11 week" long journey (it certainly took me longer than that!) was one of the most elevating educational experiences I've had to date.

von Mark A S

•Jun 09, 2018

This class is a basic course in Machine Learning - "basic" as in foundational, not as in "too simple": "basic" as Linear algebra and partial differentiation are "basic"; however deep knowledge of these aren't required for the course. Dr Ng is a superb teacher, and provides everything needed for even novice programmers to acquire a ML foundation. Before taking this, I started watching his older Stanford CS229 videos (still available) and they too are excellent - covering some topics more in-depth, but they did not come with graded tests or programming exercises. Half way through I started a Hinton Coursera course in parallel and quickly exited; I found this course much clearer & more accessible, and the programming work I found to be both rewarding and reusable. After each week I had an intense desire to actually find a relevant real-world problem and apply the current lesson to it.

von Jon I

•May 15, 2017

This was a very enjoyable introduction to and overview of machine learning, from the perspective of someone who doesn't want all of the detailed mathematical justification for the machine learning algorithms, but does want to see the nuts and bolts of how they are implemented. For example, we don't prove that we have the partial derivatives of the cost functions (or, even, really justify what partial derivatives *are*), but we do see how they are used as the input to gradient descent routines which let us optimise systems such as linear regression. It is an ideal course to use as a springboard for different aspects of machine learning, such as the Neural Networks course which is also on Coursera.

The main lecturer is clear, and does a very good job of explaining some of the practical aspects of machine learning, and when you might choose the different optimisation techniques.

von Cameron F

•Apr 01, 2018

I graduated from college last year with a math degree without a ton of programming experience; I'm working as a developer now and I've gained a lot of confidence with my programming ability, but I hadn't quite found a good way to satisfy my curiosity about machine learning algorithms. This course was taught incredibly well--relatively accessible while being comprehensive enough to satisfy my curiosity in situations where I wanted a bit of a look behind the curtain at how these things really worked (which is the issue with many articles/tutorials online--either they plop down equations without giving you a shred of intuition about how they work or they go right into the weeds). The workload was of course lighter than it would be in a typical college course, but honestly I was very pleasantly surprised by how much I learned and was able to retain in this course. Thank you!

von Guru S T

•Jun 24, 2017

I thoroughly enjoyed Dr. Andrew Ng's lectures. He mastered the art of teaching complicated concepts (stat and linear algebra stuff, ML is notorious for) in a simple and clear way. I loved the graphs, 3d visualizations throughout the course material. I specifically loved the real-life neural networks example (a simplified version of self driving car video) shown in one of the lectures. This course is designed to equip students with not only the foundations of machine learning but to ignite a genuine interest and love for the field, which is a really important goal for any course. The programming assignments, review materials and discussion forums, all provide tremendous learning around the concepts taught in lectures. I would suggest every student taking the course to thoroughly understand course materials even at the cost of extending your course session to a later one.

von L'EMIR O C

•Jul 19, 2017

Wonderful course! Andrew Ng has a way of helping you grasp the intuition of Machine Learning material whilst giving substantial leads into the more technical aspects. His vocabulary appeals both to the aspiring statistician and the amateur enthusiast. One ends with a thorough picture of the subject, from motivations of a Machine Learning problem, to the mathematics of regression & neural networks, to algorithmic implementation with good habits (clean code, vectorization, parallelization, etc.), to practical advice on error analysis & effort distribution. The 'coding' homework is gratifying : for pedagogical bits of code entered, we are rewarded with a graphical display of Machine Learning at work. Furthermore, every technical concept is introduced or illustrated by a clear, contemporary real-life situation or simple 2D example that sticks with you. Thank you Andrew Ng!

von Herman A

•May 12, 2016

Taking this class was quite a challenge as I took many different classes on different platforms in preparation for my going to grad school.

Professor Ng's class is, pedagogically, a very masterfully planned class. His simple but meaningful way of teaching made digestion of new information easy. He also used media to aid in teaching in an expert way, like using different colors to help keep track of variables, and using clear and meaningful diagrams.

And also, in behalf of the TAs and mentors, I'd like Professor Ng to know that they are a great asset. Without their help, most notably in the form of programming assignment tutorials and test cases, this class would have been much harder. Their step by step guidance to those of us unfamiliar with R or computer programming was essential, all while upholding the code of conduct. The TAs and professor Ng make a formidable team.

von Steve H

•Sep 14, 2019

Beautifully organized overview of basic concepts and applications in machine learning. The instructor is skilled at communicating an intuitive understanding of the algorithms and the essentials of how they work. He covers the key ideas while avoiding getting bogged down into mathematical and technical complexities. He provides diverse examples showing how the ideas can be applied to big datasets, images, spam filtering, robotics, website traffic, etc. The course is enjoyable and entertaining.

Most of the math is at the linear algebra level, with occasional mention of calculus. Students who are very comfortable with Matlab/Octave and linear algebra will have no trouble following the course and completing the programming assignments. Students who are unfamiliar with linear algebra will probably struggle and should brush up on those skills before starting the course.

von Ahmed A

•Sep 13, 2018

The course was extremely beneficial for me.

The tempo of the classes was optimal. Not super fast that you miss details and also not super slow that you lose attention from boredom.

The content is very well selected. In a nutshell a student can get a very good broad view on various topics. I would call it "To the point".

The programing excercises were the best thing because it gave me an opportunity to apply what I theoritically understood and play with the scripts a bit with debugging mode to deeply understand the concepts being executed in front of me. That helped me verifying my understanding and made it hard to forget what I learned.

I have to admit that some of the content I already read about before but it wasn't until going through the course that I started to deeply understand what I read before. You know I had a lot of "Aaaaah okay that explains how it works" :-)

von Heiko S

•Nov 14, 2016

Andrew, a big 'thank you' for teaching this class! Even though I DON'T consider me as an (real) expert in ML now, I got a thorough understanding of many many topics of ML. Prepared with an arsenal of algorithms and a lot of great advice I feel ready for take-off. The course has been teached in a perfect order, explaining not only the mathematical underpinnings (luckily not in depth) but focusing on the intuitions, rationale and use-cases behind the different algorithms. The many given examples (lecture and ready-to-run-code in exercise) have been very motivating and inspiring.

Some exercises turned out to be a bit time-consuming when trying to implement them in vectorized form. The catchiest part has been installing Octave (4.0.3) on OSX. I strongly recommend to go with the virtualbox/vagrant/xquartz based installation (see resources). So that's it. Thank you again!

von saurabh k

•Jan 26, 2017

I am Wondering Before I Don't know how to Implement Recommendation Features and Suggest Products On Home Page as Much Targeting the User We Have done Lots of Effort and Design More Complex Database tables and Structure but i but we did not Mining The Correct Data as per Users Views and Choice Thanks To This Course it Help me more and more in Redefining my Programming Technique and Think To Move through the Artificial Neural Networks and NLP For Getting More Relevant Search dataset collection For Targeting More and more users and thank to you also Coursera team and Respected Prof. Andrew ng He is Delivering an very Nice Tutorial Session and Bunch of Assignments which is actual relevant and Genuine Example which are Understandable to any new student who Join this course and Thank you for this course forum moderator also they are really nice .

Thank You

Saurabh Kashyap

von Guillermo A

•Oct 07, 2018

The course is really well structured and easy to follow if you have basic knowledge in any language programming language. Basic concepts of probability and calculus might also be handy for understanding some of the proofs or derivations, but are not needed at all to follow the course. I particularly liked how you get to implement some ML algorithms yourself and see them in action. It is also worth mentioning that the programming exercises just prompt you to complete the key parts for each assignment, so you don't have to code everything from scratch. For example, for the problems, you are usually given the functions to load and visualize the results, and your task is to implement just the logic for the task. One thing that could be improved is that after week 6-7, there are no lecture summaries after each video, which I found very helpful during the first weeks.

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