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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....

RP

18 мая 2019 г.

This is the best course I have ever taken. Andrew is a very good teacher and he makes even the most difficult things understandable.\n\nA big thank you for spending so many hours creating this course.

SB

26 сент. 2018 г.

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

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автор: Charles B

•8 июня 2018 г.

Andrew Ng presents the major machine learning algorithms, explaining them from the ground up. The level of detail in this courses was great from my point of view: it skips some of the calculus (e.g. doesn't show working for most derivates), but does explain the linear algebra involved. This is important because the parallel processing needed to implement machine learning efficiently relies on use of matrices and vectors - as done in the coding assignments in this course, which were a lot of fun to solve.

The assignments are in Matlab, which isn't a mainstream programming language; but I found it easy to learn and it does lend itself well to applying vectorisation techniques. Overall, I found this course really engaging and I now understand details of algorithms like linear/logistic regression and neural networks that I'd previously taken for granted.

автор: Ryan M

•7 февр. 2016 г.

This is a truly superb class. Professor Andrew Ng's lectures are clear, well-organized, and exceptionally informative. He's more than a brilliant researcher: he does a terrific job of presenting complex machine learning concepts in simple terms that a very easy for anyone to understand as well. I would add that the programming assignments are also very useful because they focus on core concepts and really help to reinforce the lectures. All around this is easily one of the best and most valuable courses I have ever taken, and I would be very quickly to recommend this course to any friend without reservation. Whether you're actively working with machine learning systems as I am or hoping to get into the field or merely curious, Andrew Ng's machine learning class is a very valuable class that covers the essentials and covers them very thoroughly.

автор: Jordan S

•3 мая 2020 г.

This is a truly great course on a fascinating and important topic by one of the world's leading experts. Andrew Ng chooses the most important topics in a field that is rapidly evolving. He goes into depth on various supervised and unsupervised machine learning techniques. He discusses the mathematics of the algorithms, how to implement it in code and examples of real world applications.

The coding exercises were surprisingly challenging. The amount of code students need to write is quite small, but the matrix operations can be tricky to get right. My only real issue is this class is that the coding exercises are in a language called Octave. Andrew Ng defends this decision by saying that is is well suited for introductory level topics, but I still think it would be better to teach the course using a more commonly used language such as Python.

автор: Yan L

•13 мая 2017 г.

There are tons of ML classes on internet (even for free), but this one is the A)most comprehensive topics coverage, and B) easiest to understand.

I am not saying your will become an expert of ML (obviously) but you would get a full picture of what ML is doing and how will they improve your daily life and work. You still need a lot of math/programming experience to build your own ML system or application software. Having said that, by taking this class, you know where you should start. The 2nd comment is of course a compliment, and requires years of hard work and practical experience to explain such a esoteric topic to everyday people like you and me through simple word and a well-structured agenda.

If you have zero knowledge in ML/programming/calculus/linear algebra/statistic like me, please take it and am sure you will get more than you expected.

автор: Jathavan S

•7 янв. 2017 г.

I think this course is the best entry into the concepts of ML you can find. Andrew Ng is a wonderful, passionate teacher and explains most concepts in a way that is easy to grasp. I liked the emphasis on supervised learning and taking time to explain the very foundation with linear regression, logistic regression in great detail. When you look around the internet everyone is talking about cNN, Deep Learning, GANs, ... but the truth is - you first need to get your foundation right before moving on the more advanced topics. ML is actually a lot of mathematics, stochastic - so getting initial knowledge about these topics is important. Some things in the course where not that well explained, for example Back Propagation. In general I can recommend this course to anyone who wants to START with Machine Learning and needs some orientation on the subject.

автор: Alexandre S

•17 янв. 2021 г.

Overall, the course appears dated but is very interesting, very informative, very well taught, and very relevant. The course is a bit old now (2011?), which is apparent in the relatively low quality of the video and audio. But since it deals with the basics of machine learning, it is still 100% relevant to 2021, and the audio/video quality is good enough for purpose. There are some minor mistakes here and then that could be corrected, but it is understood that Prof Ng has moved to other ventures and so won't be recording the material again. Mentors are quick to answer and help students. Quizzes and programming exercises are of suitable difficulty. I did the entire course at 1.5x playback speed and could finish it in about a month, partly over holidays. Only disappointment is that there was nothing on decision trees (Random Forest, Boosted trees).

автор: Jimmy G

•10 дек. 2016 г.

This course was amazing in many different ways:

1. I really learned a lot about how machine learning works (at least for the algorithms covered in the course). And I'm really keen to continue learning.

2. Learning tools and methods: anyone could just read about ML and algorithms, but the way the course is focusing on exercises really helps assimilate the content

3. A lot of effort goes into providing support to the students. I'm very thankful to the staff who are VERY responsive on the forums. They also provide a lot of test cases and other help to better understand how to complete the exercices

I recently left my job to focus on personal development and am doing a lot of online courses right now. This course by far the best one I did so far. I'm really glad I took the time to do it. And looking forward to learning more on the topic

Thanks again!

автор: Ankur S

•16 июля 2017 г.

This is a fantastic course for beginners. The only pre-requisite you need are fundamentals of high school math (Matrices, Vectors & their operations) and some basic computer programming knowledge (any language is fine as long as you can understand variables, arrays, for-loops & functions). Prof. Andrew Ng's teaches basic & advanced concepts in a manner that is easy to follow. What helped me the most were the programming exercises at the end of each chapter to help understand the different algorithms and the parameters that define them, better.

A few recommendations for this course.

It would be great to include a couple of more programming exercises, especially for un-supervised learning. If not in the main chapter, then maybe in the addendum.

It would also be great to have the concepts tested with more questions at the end of each chapter.

автор: Rubén C

•31 мар. 2020 г.

This course is a great introduction to Machine Learning. It guides you through the most relevant machine learning algorithms and techniques and gives you insight into the mathematical essence of each algorithm, until the point that you will be able to program them (a basic version) yourself in the Matlab programming language.

It also teaches you how to approach large scale machine learning systems (at a general level), and gives you practical tips for evaluating your algorithms, how to work with real-time incoming data, among other useful concepts.

I had a great time. Andrew, the professor, is charming and you feel that he truly enjoys teaching. He is so good explaining the concepts in a simple way. In just a few minutes he is able to teach you complex concepts. He gives you both the intuitive understanding and the mathematicaly formal one.

автор: Adrian L

•4 сент. 2017 г.

Great introduction to machine learning. The videos were very good at breaking down the different concepts and algorithms. It was very helpful to have summary notes available. The quizzes were useful to consolidate knowledge. The programming assignments were at an appropriate level of difficulty, for the most part, where they required some thought but were doable within a reasonable amount of time for a beginner.

The one thing that annoyed me is some of the videos were somewhat sloppy in terms of editing. There were parts where some of his narration was re-recorded but not spliced together properly, such that it was repeated. There were also a fair number of errors in the slides, but these were mostly corrected in the errata. Overall, not a big deal, but it seems like it shouldn't be too much work to just splice the clips together properly.

автор: Ignacio F M

•16 апр. 2020 г.

It is a great course in order to begin with Machine Learning. Covers every basic aspect of this field, even every some more advanced topics, the explanations are very good and the practical exercises are interesting. The course is well suited even if you do not have a good level of programming or Mathematics.

Though, some of the aspects that I think could be improved are that the questionnaires are maybe too easy and short, and that in at least one of the practical exercises the student should work on how to build the script to put all the different functions to work togheter.

And if you already have knowledge on Machine Learning and your level of mathematics is higher, then the course is still good since it will cover all the basics and fill some gap that you could have, even though there might be more appropiate courses on this webpage.

автор: Saravanan T S

•18 мар. 2017 г.

This course has been highly delightful to learn through the concepts of machine learning. For someone like me having 17years programming experience, and with some hands on of neural networks from colelge days, and analytic tools for a few applications in R; it is a great refresher on the fundamentals and great breadth of practical technology elements that are most useful in machine learning applications.

Andrew's teaching method is great. His clear and simplistic delivery style of complex concepts with apt examples ensures the student grasps the essence with ease and works navigates his way through complex algorithms with confidence. I would love sitting through his lectures in the future too!

Congratulations to the entire team that put together this course and making this a great service that is available for anyone wanting to learn ML!

автор: Justin Y

•9 февр. 2019 г.

This course is wonderful and charming. By taking this course, I got the basic knowledge of machine learning and artificial intelligence. Concretly,I knew what is supervised learning and unsupervised learning and I also learned how to operate that by myself through a fresh language called Octave,which I never used before. Besides,I also want to express my thanks to Prof.Andrew Ng for his kindness teaching and shareing his knowledge in this field. Not only his broad knowledge but also his skillful teaching method that impressed me a lot. He could always taught us many difficult and obscure conception by taking some simple and clear examples,which can let us understand easily.

I will continuously take a course by Andrew Ng called Nueral Networks and Deep Learning, and I will dedicate myself into this course and I hope I could learn a lot.

автор: Francesco P

•14 нояб. 2020 г.

Dr. Andrew Ng has the unique ability to explain complex and articulated concepts with incredible ease and effectiveness. Throughout the entire course there's never been a moment where I felt lost because each topic was explained very well and even though several years have passed since this course was released, it remains maybe the best one to get both all the theoretical foundations of Machine Learning and getting the hands dirty by implementing the algorithms in a low-level fashion using the Octave language. It certainly is difficult but it helps you understand all the little things that otherwise would've been difficult to understand by only watching videos about theory.

It's been an amazing journey, thank you to both Dr. Andrew Ng and the amazing community of learnerns that made the learning process a little bit less hard.

Francesco

автор: Angadbir S

•19 апр. 2020 г.

The course is an excellent introduction to the Machine Learning techniques. While the field is evolving by the day, having the experience to code the basic and powerful algorithms by hand provides a fair confidence and intuition of the inner workings of these cool-sounding techniques. The subtly introduced idea of Matrices as efficient computation model was very interesting having read it back last time in high school math without knowing what their real life application was. Immense gratitude is in order for Andrew Ng (and probably team) for having created the course that helps one focus on coding the actual algorithm rather than the data preparation and other data flow problems (which I understand is more than half the time spent for a practitioner). This is a must-do course for people thinking of starting to learn about this field.

автор: Rohit K

•17 июня 2019 г.

This is the course for which I have joined Coursera initially. Professor is very elaborate in explaining anything. I am a student from core Mathematics and Statistics background. But I feel that the course is designed in such a away that any students without having a high school level knowledge of basic mathematics and programming can grasp the ideas discussed here. But I would recommend this course as an introductory course to those who are rather interested in the core mathematics behind Machine Learning. For me, It was a good course to get a overview of Machine Learning as I didn't have a proper course in ML earlier. The positive thing is, a positive interest have been built regarding ML as I was learning from Professor Andrew. I will definitely go further to learn deeper in mathematics of ML and it's improvements down the years.

автор: John D B

•16 февр. 2020 г.

I enjoyed this course a lot. I found the lectures to be pitched at an appropriate level and were not boring or rudimentary. I would have liked there to be either links or optional lectures to the math and derivations for some of the algorithms that were glossed over in the lectures (such as backpropagation of neural nets) for those of us with the math background and interest, but I agree with the general approach of not bogging the class down with these full derivations. I think Andrew Ng's explanations/intuitive justifications of the various algorithms were what made the lectures really special. That intuitive understanding is much more valuable than going through a long derivation. I thought the programming assignments were excellent and impressed that the computer grading worked flawlessly. ( I used the MATLAB online option.)

автор: Rohith R

•17 июля 2020 г.

This course is definitely one of the best out there to take for a beginner in ML, especially since most of it is free. If you have prior knowledge of ML and how things work, this course is probably more of a refresher. However, if you are a beginner like myself, you will gain a significant amount of understanding of how various ML algorithms are implemented that you would not have otherwise. This course would have been AMAZING if it was taught using Python but it was still a great learning experience (although I can tell I'm not a fan of MATLAB). I am so happy I took this course and am confident to say I have come a long way from being a novice to being competent. A HUGE thanks to Prof. Andrew Ng for creating this course. I hope to take more of his courses in the future!! He just makes the hardest concepts sound a lot more simple.

автор: Pranav S

•1 июля 2020 г.

This course has to be one of the most magnificent course that I have ever taken. I spent sometime on other online courses on machine learning but did not complete them because the tutors didn't connect with me, the way Andrew did and that has been one of the primary reasons why I love this course and as a result of which the subject Machine Learning as well. Behind every successful student there lies a passionate teacher guiding him towards success and Andrew has had that influence on me. Thanks a ton for making our lives more exciting and for encouraging us to keep dreaming big. This is the best course and I highly recommend it to everyone who have prior programming experience and have interest in Machine Learning. Cheers! Thanks to Coursera too for providing Financial Aid which helped me pursue this course in the first place!!

автор: Fernando N

•13 июня 2018 г.

Great fundamentals course. If you know your fair share of mathematics and optimization concepts you will definitely be more comfortable, but Andrew Ng makes great strides in providing conciseness for these complex topics and algorithms. I am an Industrial Engineer and so I have come in to the course with the mindset and understanding of these optimization topics, but was new to many of the applications within machine learning. I have taken a few other machine learning courses, and in retrospect I believe prospective students should start with this course. If you are not familiar with linear algebra, Andrew goes through a refresher, so even that is covered. Only difficulty in the program is the programming itself. I am a self taught programmer, so that didn't stall me, but that is the only thing I could see holding students back.

автор: Patrick B

•27 дек. 2020 г.

The material might be a bit old, the video and audio quality not living up to today's standards, and Octave is a tool many won't consider as a starting point in machine learning. However, the material is presented in a way that it's easy to understand and motivating to continue working on the course. The way the graded programming exercises are implemented is just great; the system gives you a fast feedback and works flawlessly.

The single complaint I have is the presentation of the neural network algorithm. This is probably not only the hardest thing to understand, but also the hardest thing to teach, so I'm complaining at an extremely high level. But maybe the videos could be improved to get the main points a bit better across. However, with the tutorials and forums, it's possible to figure it out anyway. So five stars anyway!

автор: Sauro S

•1 сент. 2020 г.

Thank you Andrew Ng for this great course. I had a bit of research experience with regression analysis, neural networks and PCA, but this was my first "real" introduction to the fascinating world of machine learning algorithms, and I am greatly satisfied! I found the course to be really well structured. it begins with "simple" linear regression models, and progressively builds up to more complex / elaborate systems. The subsequent lectures not only present new models, but also gradually uses the accumulated knowledge presented previously, in addition to pointing towards important aspects to consider when designing / tweaking a real-world model. Again, thank you very much. I can't wait to learn more about machine learning techniques, and to apply them to my own research work in biomedical engineering and human motor control.

автор: Juan A L

•11 июля 2017 г.

Great course with a lot of useful machine learning concepts. Good balance between theory and practice. Andrew Ng guides adequately each one of the lessons with good examples and fruitful suggestions either for the better comprehension or for the better application in a professional/practical concepts application. Some other fine machine learning algorithms could be presented here (decision trees, bayesian networks, hidden markov chains, genetics algorithms, etc), nevertheless it is understandable that the course has a specific scope either in a group of topics either in time, so no regrets about not finding more machine learning subjects here, it represents my new learning backlog for the future. Very satisfied with the material, the course structure, the exercises and the teacher. I will recommend it to my engineer friends.

автор: Daniel N

•11 апр. 2020 г.

Excellent course. Very good introduction to numerous facets of ML. I supervise and generally work with a group of data scientists, data engineers, and ML engineers for one of the biggest companies in the world, but my background is more tailored to translating customer requirements, owning the vision for product development, filling in gaps, removing blockers, etc. - this was a huge leap forward for me in terms of speaking the language. Huge thank you to Professor Ng. Only complaint is the lack of emphasis on the actual iterator variables used in highly involved summation algorithms. At point, it was even stated that the iterators (i, j, k, etc.) and the total counts (n, m, etc.) were not that important. THEY ARE, pay super close attention to which one is which is Professor Ng walks you through it, and it will help a lot.

автор: Daniel G F

•20 нояб. 2016 г.

Amazing course completely worth every second spent on it. I was enrolled in a similar subject in my University and I decided to unsubscribe from it and do some other stuff to get the credits as it involved so much time due to its really long practices (mainly due to documentation, dealing with Python peculiarities and collaborating with really junior or low performing students) that it was impossible for me to get the time to properly work on it, as I'm also working and doing some other subjects.

With this course here I have been able to learn the same concepts and work with them in practice too with much less overhead, focusing on the most important parts and concepts for each topic.

Most importantly, I this has served me as an introduction to the Self Driving Car Engineering nanodegree that I hope to join soon at Udacity!

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