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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....

HB

Sep 16, 2020

Loved the course. Andrew Sir explains the intuition behind the concepts really well. Excited to continue with the rest of the courses by him on my way to becoming an AI Engineer.\n\nThanks a lot, Sir!

AA

Nov 11, 2017

Great teaching style , Presentation is lucid, Assignments are at right difficulty level for the beginners to get an under the hood understanding without getting bogged down by the superfluous details.

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автор: Sayan G

•Oct 12, 2018

Firstly, I would like to thank Coursera and Professor Ng for making this course available for people like myself, who have been observing recent shifts in the information technology industry from within. It has allowed me get to know, albeit at a very basic level, the machine learning algorithms being used currently in the industry, and also take beginner steps towards being able to implement them using a programming language.

Secondly, the course acts as a springboard for more specialized programmes which one might want to train oneself in. Personally, I would like to know more about Neural Networks on the Supervised Learning side, and Recommender Systems on the Unsupervised Learning side, and the information already presented herein allows me to get a head start.

I intend to make extensive use of the information I have gathered from this course, as I work towards my goal of becoming a Data Science professional.

Best Regards,

Sayan.

автор: Li Z

•Jul 27, 2017

Highly recommended to anyone who wants to understand what machine learning is about. This is by far the best teaching material available online that I know as an introductory class to machine learning. I know some python programming and very little C before taking it; I tried to read the codes on Kaggle website to understand their projects, but only found myself not understanding anything when it comes to data analysis with machine learning. My friend recommended this class to me and I am glad I spent three months to study it. Never done any matrices calculations before, but it is not hard to understand it; and I have forgot most of my college math (major in basic science research in the past), with some help (online and friends' ) again that's not difficult to understand the content either. Now I am excited to learn some more advanced machine learning skills and hope to do some projects for practice. Thanks for this great course!

автор: Dimitar D

•Dec 06, 2015

The course provides a sizeable amount of pretty cohesive material, which can still be understood by non-CS students. It's very practical and it includes a very nice mix of quiz tests and great MATLAB/Octave programming assignments. After going through the assignments I started wondering about other problems which data sets I can plug with small modifications into the completed solutions. Andrew Ng keeps a great balance between explaining important details and skipping over parts that require straying too much from the main topic of the lecture. I still don't have very deep or broad knowledge in the Machine Learning domain, but it feels like the course doesn't miss anything of the fundamentals.

Overall, I'd definitely recommend the course to CS students, high-school students with interests in the computer science area and even specialists in other areas with some knowledge in linear algebra with interest in the AI and ML domains.

автор: Guo X W

•Jul 06, 2020

Had high expectations prior to taking this course because of the rave reviews. This course truly exceeded expectations. Andrew Ng explains ML concepts in a simple, intuitive manner. He tells you exactly what you need to know, and yet he provides optional videos for students who are more mathematically inclined. The programming assignments are very well-designed, and serve to reinforce understanding of key topics. I also appreciated the nice balance between theory and practical aspects of ML (e.g. how to debug, pipeline of a ML project)

Personally, I would have preferred if less starter code had been provided. I thought it would be useful to learn about how to create a machine learning application from scratch. It's also been many years since the course was first introduced. Would be great to do a renewal of it with more high-res graphics and state-of-the-art examples!

Thank you Prof Andrew Ng, I've learnt much from this course!

автор: Nikolay S

•Oct 21, 2016

It would be easy to rate the course with anything below 5 stars for it being not enough formal and academic; for explicitly not requiring prior knowledge of basic calculus and algebra; for using MATLAB instead of actual industry standards.

But that would probably be unfair, because Andrew Ng's course is a brilliant introduction to Machine Learning. Soft, tolerant approach helps newbies to overcome initial feeling of being overwhelmed by ML algorithms and learn them while playing with lots of code provided with the course.

It is also definitely worth mentioning that prof. Ng not only explains problem settings and algorithms that are suited to solve them, but also shares many experience-driven hints for actually applying ML in practice.

All in all, this course will not make you a Data Scientist, but it definitely will help you grasp the basics and prepare you for more demanding education; or even for simpler actual practical tasks!

автор: AbdulSamad M Z

•Dec 22, 2018

Machine Learning by Andrew Ng is one of the best courses I've ever taken - hands down.

The course is extremely well-organized and thoroughly covers the wide range of topics required to get a solid understanding of machine learning not only in theory, but also its applications and, more importantly, how to implement and debug it. The lessons cover a particular part of each chapter and are crystal clear. Although audio quality is not top-notch sometimes, the subtitles are there to help. The quizzes and assignments supplement your understanding by getting a hands-on experience on how to implement each concept and optimize your implementation. The assignment questions are crystal-clear in their requirements and contain additional code that visualize your work thus allowing you to focus on machine learning concepts and understand every teensy little concept related to them.

This course is worth every second and penny spent on it.

автор: Anton S

•Jun 02, 2017

This is a brilliant course. I hardly can express my experience in that short review. But I clearly feel that Andrew Ng is a very dedicated and talented teacher, as well as a great ML/CS professional. As a bit of PhD student myself I know also that mr. Ng is a real influencer in ML field, being the (co-)author of namely LDA, which is a fantastic idea. With mentioning this I just want to say that you hardly can find a better lecturer for ML. For the course, it is well-balanced, dense enough, with both in-depth and overview topics, acceptable complexity for programming assignments, and really holistic view on both research and applied aspects of the fascinating field of ML. I highly recommend everyone - whether you are a gonna-be ML engineer/researcher or just a curious CS/IT profi - take this course! It's high interdisciplinary and definitely worth learning, giving you a great insight into many issues of modern science.

автор: Stian R S

•Feb 02, 2017

Excellent introduction to different methods in machine learning. I have some prior experience with machine learning, and although this is an introduction, it gave me a lot of good tips for implementation, debugging and workflow. It also gave me a deeper understanding of the different concepts in machine learning and when to apply the different methods. Questions during the lecture videos and quizzes afterwards keeps up your attention, and the programming assigments make you understand more deeply how to implement and apply the methods on real problems. The programming exercises are mostly pretty easy if you have some experience with programming and matrix/vector multiplication, and some of them are really funny to play with and apply on your own data or pictures afterwards. The lecturer, Andrew Ng, is also very good at explaining, and you never feel that he jumps over unclear details. I highly recommend this course!

автор: Paul P

•Sep 22, 2020

Machine Learning was just the right amount of challenge for me, and really forced me to think both analytically in terms of what algorithms and programs are doing, and holistically/strategically, giving me some tools to anticipate common problems in machine learning system design. I do believe having a bit of background in mathematics and a beginner's grasp in programming helped me in the beginning, so if you don't have this knowledge, just be prepared to spend a bit more time on the material as well as brush up on some theoretical basics, such as linear algebra basics and sums/derivatives. The estimated time for completing each week of the course doesn't include the homework, which I found to be the most practical, useful, and challenging part. I would allocate 2x the estimated course time for each week if you include the homework assignments, unless you're already fluent in matrix multiplication and programming.

автор: Biplob

•Jul 31, 2020

This course is a great introductory course on Machine learning. You don't need to know much math as everything is taught to you; if you know some basic calculus then you're fine but again, you don't really need to as it is explained. Having an introductory background in linear algebra is an asset but not a requirement. Specifically it helps to know something about multiplying matrices and the resulting dimensions but all of that is taught in the course. Give yourself enough time with the programming exercises and read the discussion forums first before attempting the exercises because they provide clarifications in some cases. I strongly encourage taking notes because you'll refer to them constantly during your programming exercises. If possible do this course with a friend so you can have weekly discussions about what you've learned, can support each other when needed and finish the course on time. All the best!

автор: 조대현[ 대 / 인 ]

•Mar 30, 2020

I majored physics in undergraduate and now majoring Artificial Intelligence in graduate course. I was having tough times getting through this new realm that I don't know very well because I was studying this for just hobby. The knowledge that I learned before was only from some articles and books without any practice but through this course, I now have organized concepts of what machine learning is and how they are used in practice. Many of you might have trouble with python because numpy is quite tricky library to use with start. But intuitive octave code made me successfully focus on the course topics not on manipulating numpy.arrays(because it takes so much time to do that). Last video was impressive because Andrew said he also had hard time studying this field and I'm totally related to that. Thank you for nice video and thank you for making me feel that I'm not alone in this field. I'm really appreciated! :)

автор: EKTA

•Jul 15, 2019

It was a wonderful experience completing this course. The review questions present inside the video, the quiz and the Assignment, All these together made the journey even more interesting. It boosted up my confidence that I am getting the things rightly. Few times, Programming Assignments created obstacles as it was not accepting our code which seemed all right to us but as they said, the code needs to be correct for every type of data. So the assignments when completed, used to make proud as well. For help, there are many options available as test cases, mentors.

I enjoyed it thoroughly. Special mention to Sir Andrew Ng, I couldn't expect more. He is just the Best I can say. Thankyou is a very small word to express for gratitude.I really got connected with him. Thanks a lot sir.

Last but not the least. thanks to coursera organizers who are doing a wonderful job. I will surely start a new course once I get time.

автор: Raju K

•Mar 28, 2019

Great course. Really enjoyed watching and listening to Professor Andrew. The course material, quizzes and programming assignments are of very high quality. The programming assignment may require some time and effort if you don't have programming experience. I am a software engineer so It was relatively straightforward for me. The programming problems were very good to reinforce the leanings and it was enjoyable to see the code you write working on real-life solutions. The mathematics is not too heavy if you can remember junior college matrix algebra and a little bit of calculus. The course does not expect you to know in-depth details. The course covers a wide variety of machine learning basics in a short, concise and effective way. I notice, "concretely" is the most often used word in the lectures and it follows with a practical explanation of how to apply and implement the concept learned. Great job, Coursera.

автор: Ivan M

•May 17, 2020

Andrew Ng is one of the best teachers I've ever seen. Serious and direct, but also accessible and very motivating. Thank you so much!

The course shows you the basics of Machine Learning in a quite mathematical way and includes a short Matlab/Octave course. The programming exercises are long, although some of them have too much explanations and too few code to fill. Maybe the vectorized form of the expressions should be shown in the course before having to implement them in the exercises.

Apart from the theory, the course has a lot of good tips for doing real-life machine learning. I think Matlab/Octave is a good choice to teach the contents, although I never worked with this language before. It's really easy to apply the learned concepts and you always can take another course of machine learning for Python or R, because you now have the knowledge you need to implement it in any language.

Thanks again, Andrew Ng!

автор: Alexander B

•Sep 12, 2015

Amazing course! Well worth the time. Opens so many possibilities and springs so many new ideas! Andrew is a great instructor explaining very complex concepts in extremely simple way.

One thing I would probably add to the course is something about existing platforms/software for machine learning. It is a bit more on the practical side for the people who will choose not to code the algorithms by themselves but would prefer to use existing solutions.

Thank you very much. I also think that thousands of students passing this course every semester is a great tribute to coursera. I am a CEO of a tech company in China and while I could access some of the best education resources financially, I can not afford to do that because of lack of time. Coursera is a great platform that gives me that access. I think for people like me it could also be great to open up some possibility to contribute back to coursera in some way.

автор: 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.

автор: 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.

автор: 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.

автор: 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!!

автор: Gaetan P

•May 09, 2020

Many thanks to the professor Andrew Ng for his time and effort to make these videos.

This course is perfect for beginners to understand the key concepts behind ML. It is quite reasonably easy in terms of mathematical and programming skills required (standard linear algebra knowledge is sufficient). The videos won't mention inessential mathematics, because the point of this course is to focus on the practical implementation of several algorithms in real-life examples.

In practice, start with this course to get all the main principles, and then learn to use more pratical engineering tools (like TensorFlow, Spark MLib, or Matlab machine learning apps)

I highly recommend the 'useful resources' section, to learn more advanced notions, and apply what you learned to other programming languages (python, C++)

One suggestion: the audio recordings have not optimal quality, they could benefit from a cleaning process.

автор: 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.

автор: 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.

автор: 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!

автор: Selvaprakash

•Jun 25, 2020

Before starting this course, I know only a few concepts in Machine learning but after finishing this course, I know how to apply the most advanced machine learning algorithms to problems such as anti-spam, image recognition, clustering, building recommender systems, and many other problems.

The biggest takeaway for me is that I learned, the amount of attention needed to evaluate the Machine Learning algorithm's efficiency and also how it correlates with the time and effort I should put into the specific system component.

Basic Linear Algebra is a pre-requisite for this course and you will be working in Matlab/Octave. If you know Linear algebra, which is essential for ML and little experience in Matlab/Octave you can finish this course with flying colors. I would recommend this course to get to know the concepts of Machine Learning, as always you should practice a lot to master all the concepts. :)

автор: Deepak S

•May 30, 2020

This was one of the most interesting course I ever took. It was long time pending, I started it but in initial days I was discouraged as I was not able to match the mathematical understanding even I after having engineering background and took sometime to brush those concepts, but after some time and some reading I was able to understand. I was putting hard efforts and after 3rd week it was more difficult in doing programming assignments, which again took some time to learn and the cross this difficulty level. Overall it was great experience and great learning. I can't believe this that I reached till end. The course was ending but my interest and curiosity was increasing everyday. Even sometime I was dreaming the problems I was trying to solve and overcoming those challenges. It was amazing and throughout I enjoyed this course. It is totally mind blowing and addictive one. Thanks Mr. Andrew Ng.

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