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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....

AF

16 мар. 2021 г.

I want to thank you very much for such a great course in any aspect especially from professor Ng . I just want to suggest that it would be great if there was a final project for the end of the course.

AB

30 авг. 2020 г.

A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.

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автор: Himanshu G

•25 апр. 2020 г.

Thank You Very Much, Andrew Sir. I feel the scarcity of words to express my gratitude and thank you to you, Sir and the Coursera. The course is very well designed and enables a novice like me who has little prior exposure to the field of computer science to understand the concepts of Machine Learning, pass the review exercises and programming exercises. I think a little improvement could be if more videos related to Mathematical concepts (for example something relevant to Support Vector Machine) relevant for this course are included in the course or authentic links or (esp. Mathematical) resources are provided which students can refer to sharpen their mathematical concepts which are relevant for this course. Thank You very Much, Coursera for providing me financial assistance. I guess without the financial assistance, it would have been hard for me to enrol to this course because I earn very modest from my current job. Thank You Very Much Coursera and Andrew Sir.

автор: Josh F

•25 нояб. 2017 г.

Excellent course. I have no background in math (save for a good understanding of linear regression) but professor NG's teaching is so good I was able to follow along quite well. I knew I would eventually be working in python, so I personally elected to forego the assignments in matlab/octave and found a resource online that had all of the finished assignments in python which I simply studied and commented until I understood them. I would recommend this approach if you are simply interested in getting up an running quickly and know you will be using python. I would also recommend watching the videos at 2x speed to save time.

The only criticism that would be possible to levy would be that he did not go deep enough into the math in some areas but on the other hand, I may have been lost if he had. I was really appreciative that he was encouraging enough to say "it's ok if you don't fully understand the math, it will work regardless". Solid course, absolutely amazing,

автор: Kai C

•10 февр. 2021 г.

I LOVED THIS COURSE. SO. FRICKING. MUCH. Professor Ng is very thorough and obviously cares a lot about what he's teaching. I feel like I have a really solid foundation in the concepts of Machine Learning after taking this course, and I would highly recommend it to anyone else interested in it.

A word of advice to prospective students -- linear algebra is your friend, and 3Blue1Brown on YouTube's Essence of Linear Algebra playlist is of near infinite value as a companion to Professor Ng's instruction.

This is definitely a course where you get out what you put in. If you're willing to work through the quizzes and maybe take out a paper and pencil to diagram out some of the matrix math, as well as grind out the programming exercises, you can learn a LOT. Professor Ng is one of the leading experts on Machine Learning in the entire world, and the fact that I can learn about this field from him for FREE is absolutely incredible. So thankful to have taken this course.

автор: Tianhong Y

•23 нояб. 2016 г.

Prof. Ng is such a good teacher that he explains things in a proper way to make you understand.

He has a profound understanding of the details and derivations behind the knowledge and conclusions. If you have a relative background, you would have a chance to think about the knowledge in a deep way. If not, you can still get the main idea and be able to use it, and you know what is lack and where to learn it.

Overall the lecture notes and videos, the quiz and assignments are all good, full of thought about how to make students follow the course and understand better, as well as exercising with real applications.

I didn't realize that the machine learning course was the first one on Coursera and Prof. Ng is the founder of this wonderful platform, until toward the end of the course. No wonder the quality of this course is so good. I learned a lot and would recommend it to anyone with a good math or physics background and want to learn Machine Learning seriously.

автор: Daniel A R

•1 мая 2017 г.

As this course is rated, and according to the lots of opinions written about this course, I can only add a new congratulations remark to their creators. Andrew Ng is not only a genius who masters all the contents, he is also really didactic and teaching. Andrew is able to boil the more complex concepts (e.g.: neural networks) in simple explanations with very illustrative material and an updated approach to real examples and use cases like (autonomous drive or Photo OCR and text recognition).

I would like to thank you the great support provided by Tom Mosher in the Discussion Forum (this is one of best forums I've checked in the different MOOCs and the main reason is the fantastic work done by Tom who gives quick and intelligent answers focused on making you think and learn about the questions or doubts you ask).

I think this course is a must for all those who are into data engineering, data processing and especially Machine learning or Artificial intelligence.

автор: Bob H

•8 нояб. 2018 г.

Excellent course, Professor Ng teaching approach works very well for complicated but fascinating subject. I always found his lectures to be clear and concise regardless of the difficulty of the material. I also found the programming assignments to be a valuable tool to enhance understanding of the material.

At the conclusion of the course I feel I have an excellent grasp of the topics that were presented in this course. I have found additional materials on the internet (e.g. course syllabus from CS 229 that Professor Ng teaches) such as papers and books covering aspects of Machine Learning. I am now equipped to continue my learning using more advanced material. I am now rereading the Master Algorithm by Professor Domingos and I find that I now have a improved comprehension of the material presented in the book.

The only thing I could wish for is additional material and assignments for other learning approaches, e.g. Markov Chains, Naïve Bayes etc.

Thank you!

автор: Sonya L

•13 мар. 2021 г.

This course has good learning material, home assignment, companion material as well as resources. I consider it as a temple that I have to visit and to step over to lead to a bountiful machine learning world. The biggest complaint I have is that some course video and slides have a lot of errors, especially neural network part. Yes, there are errata to correct them. However, it takes time to cross check. That might misleads students before they detect them. Also some of intuitive lecture might not be concise enough and might mislead students too. Having say that it definitively worthwhile to take this course for people who haven't had formal machine learning academic background. It's very tough to juggle busy work, family and perseverate on 3-month course (not to say finish challenging home assignment) . It gave me a lot of stress. I am glad that I push through it. At the end, it is worthwhile. Thanks to Professor Ng and TAs' great works.

автор: int s

•22 окт. 2019 г.

I learned a lot from this course. I recommend any beginner (like me) or a professional in this field may try this course, because

1. I have learned types of mathematical learning

2. I have learned how to prepare myself to proceed step by step to solve an ML problem in future, instead of just jump into the problem and try to solve

3. Not less not heavy but Andrew has shown me the actual mathetics behind the algorithms.

4. I have learned to find a bug in a model and how to approach it to debug the same. Those parts are the best parts of this course I have enjoyed.

6. I learned how to decide the hypothesis, how decide the polynomials, how to decide parameters, how to decide threshold value (instead of guessing[Classification Problems]), how to choose and/or synthesis features and many more.

5. The last thing I should mention, Andrew taught me how to evaluate an algorithm with a simple number(real number) whether it is working fine or not.

Thank you Mr. Andrew Ng

автор: Jason J D

•25 июня 2019 г.

This is probably the best Machine Learning course out there. The course covers up everything in Machine Learning, right from the basics to the complex parts. Even though I had studied some Machine Learning at college, this course helped me learn many new concepts that I was previously unaware of. The instructor Prof. Andrew Ng is very good. His explanations and examples are simple, yet cover up all the details. The course structure is very good and the assignments are well prepared. The course also gives a tutorial on Octave / Matlab basics and helps develop your logic and coding skills in the same, through programming assignments. The course material like the Lecture Slides are very useful as well. This course not only helps you learn Machine Learning, but it also helps you develop the intricate details used to implement Machine Learning in daily as well as industrial applications. I would recommend this course to anyone interested in Machine Learning.

автор: Keiji H

•17 авг. 2017 г.

You can learn everything about machine learning from the very basic things to the now omnipresent product recommenders and spam removers such as Amazon's and Gmail's. The course consists of lots of short, 0-15 minutes, lecture videos and programming assignments, so you can see them at your intermediate times though you will need a certain amount of time to complete each assignment, which would greatly help you understand how they work and make you feel like you could make your own algorithms yourself. Don’t worry about the programming environment. You can see how to install it on your computer, either Mac or PC, in the course. In my case, I’ve completed all using Online Octave, in which you can run your program without installing anything on your machine because it runs online though the computing power you can use is limited. Anyway, I truly appreciate Andrew Ng, the creator of this course and the co-founder of Coursera, to give this great opportunity.

автор: Tan M

•30 окт. 2016 г.

It has been a great learning experience taking this Course. I am currently taking an advance version of Machine Learning in my school, and this course on Cousera has definitely provided me the basic and essential knowledge in tackling more advance machine learning problems in school.

To the mentors, thank you for answering my questions that i have posted in the forum. Just a little feedback, i hope that there will be different mentors tackling on different weeks' problems (Spreading the workload ..maybe). In this way, answers to some of the questions can be more detailed.

And i also hope that some days the errata in videos can be corrected even though these are minor errors...so that students do not need to refer so much to the errata page while watching the video lectures.

Overall, the course is great and i will definitely recommend this to my friends! I hope that one day Coursera will have an advanced version of the class! (etc. Machine Learning II)

автор: Brian T S

•13 февр. 2018 г.

Professor Ng provides an extremely accessible overview of AI techniques. The math does seem a bit imposing, but anyone with a background in pre-calculus or higher should be able to "get it" if they sit down and work it out. I took a similar course at the University of Texas in the 90s and this was presented in a much more understandable manner. It might have been beneficial to work on a complete AI programming assignment at some point, or at least accomplish more of the coding, as most of the assignments required completion of the more trivial aspects of the technique. Also, I was a little surprised that search (tree traversal) was not addressed. Maybe that's too old hat, or covered in a graph course. There were some frustrations with Coursera not working correctly (unable to submit assignments due to broken URL forwarding, broken Latex rendering which is still not 100% working), but I really like the site when it works. High marks for Professor Ng!.

автор: Tom M

•30 янв. 2018 г.

I reviewed many courses before taking this one and I'm sure that I made the right choice. This course covers the underlying mathematics of how the various learning algorithms work. Understanding at that level is essential to designing and debugging machine learning systems rather than just applying rote techniques or blindly calling library functions in an ML framework.

I found parts of the course challenging as I'm not a great mathematician but I'm very glad I persevered. The pace and structure of the course were just right. I've just got one tiny gripe. I suffer from multiple hearing problems so at times I needed to turn on the captioning. The problem with automatically generated captions is that they struggle (i.e. get wrong) precisely the same words that I am struggling with, so for me at least, the captions were useless.

Overall, I would describe this as the defininitive 'must-do' course for anyone looking to get involved with ML applications.

автор: Joseph M

•7 февр. 2016 г.

Fantastic. Andrew Ng is a naturally charismatic teacher with a knack for anticipating issues which his students may encounter and assuaging them before they become sticking points for later understanding. By their nature, online courses cannot benefit from students asking questions of their instructors so it is doubly important that instructors be aware of areas which may confuse students and take anticipatory action to avoid this- this is only one of Ng's strengths. Beyond this, Ng is simply an enthusiastic instructor whose passion for his subject is contagious. He also conveys a genuine sense of understanding the student's process of coming to grips with more difficult portions, often explaining what has confused him before (though, given his expertise, one may wonder just how much these areas actually give him difficulty). All things considered, the biggest disappointment is that there are not more courses available with Ng as the instructor.

автор: Prithviraj C

•14 янв. 2021 г.

This was a very helpful introduction to machine learning. The instructor's explanations were very succinct but always rigorous. He provided insight wherever possible. There are some optional videos explaining much of the underlying mathematics - and even when there were topics where some of the math was beyond the course of the syllabus, professor Ng made it a point to provide references. (I especially appreciated this as PhD student in mathematics myself). I always think it is helpful before learning a new technique to ask 'How is this applied in real life?'. I can confidently say that Professor Ng made it a point to answer this question with every new topic that was introduced. The assignments and readings are very good at helping you become truly comfortable with the material taught. But they are never too tedious to be discouraging. Not only did this course teach me a lot - it also piqued my interest in the subject. 10/10, would recommend!

автор: Leonid B

•10 мая 2020 г.

First of all - thank you very very much to Andrew Ng !!! There are some things which can be improved - as always but in general, I think it is perfect. I am a hight Energy Physicist and I have some reasonable good knowledge of mathematics. Some of the explanations are so good and so clear that I would use them while explaining quartum mechanics ))))))) and this is not a joke. I was inspired by Andrew Ng and had read partially (not entire) the Ph.D. thesis of Andrew Ng. Thank you one more time for all the algorithms you have explained here. Thanks to you I have: 1) my own spam classifier with my own dictionary - for this, I use bash and c++ library, 2) I have my own list of movies and I make these lists for friends :-) !!!!! 3) and finally, I have the vector components of my face and faces of my friends in the space of celebrities )))))). I wish you and your closes surrounding to keep healthy happy and focused on the things you like to do !!!!

автор: Tony W

•25 нояб. 2016 г.

Excellent course! There are many small mistakes throughout, but these are addressed in the errata provided. In addition, a few mentors were constantly providing helpful feedback, even to questions covered elsewhere (such as the errata). I came to the course with a strong statistics background, so one o the very helpful things for me was learning the machine learning terminology for things I was already familiar with from statistics. One final point - the course is structured so that you are improving your general programming skills as you proceed. You begin by doing the simple for-loop implementation but will later see/do the more efficient vectorization. This is incredibly helpful for those who do not come with a solid basis in linear algebra / matrix algebra, since you do the intuitive/"easy" version first but later develop more efficient coding which you now understand because you did the intuitive/"easy" version earlier in the course.

автор: Jan F

•24 июля 2021 г.

What an absolutely outstanding course! Everything is explained very extensively and clearly, many very relevant topics are covered, the theory as well as the practical implementation, and all that without being dry or boring but extremely interesting and inclusive. And all that accessible for everyone, for free... Before I started I was intrigued by the excellent 4.9 rating, and after completing this course I could not agree more, and gladly add to it! The only thing which I would have loved to see as well were proves to the presented algorithms, or links to them, for those who were interested. But of course, that would just be a nice addition, nothing fundamental, and for most people not relevant in the first place. But thank you so much for making and sharing this course as to making it accessible for anyone interested, it is more than appreciated, by me and hundreds of others. I learned a lot, and loved completing it from start to finish!

автор: Matthew E H

•1 июля 2020 г.

Andrew Ng is an outstanding instructor. This course takes a breadth-first approach, first glossing over the details to describe how various ML techniques are used, then coming back to describe how the underlying algorithms work. This approach worked very well for me. The intermixing of video lectures and straightforward programming exercises worked well to cement the concepts. I have a much better understanding of the ML domain than before I started. Over the past few years I have read a number of books attempting to come up to speed in the fundamentals of machine learning, but I found each lacking, either going too deep into the underlying mathematics before describing how various techniques could be used, or providing only surface level descriptions with no real world examples. I am so happy that I finally put the time into this class, I would recommend it to anyone wanting to come up to speed in the fundamentals of machine learning.

автор: Anant B

•11 янв. 2017 г.

This course is very well organized and exposes students to some fundamentals of Machine Learning and practical applications of them. Assignments and guidance to complete them from resources, tutorials and test cases are absolutely helpful to learn these basics and their application with an hands on approach and gives students much more confidence on what he is learning and has been able to absorb. There is a lot of complex materials covered in this class which at the beginning looked fairly insurmountable but working the Assignments with help from resources in form of tutorials, test cases to run and of course the very valuable forum discussions and moderator Tom M's continuous help made learning a lot easier than I thought. Finally thanks to Prof Andrew Ng for offering such an well organized course like Machine Learning through video lectures, lecture notes (pdf) and exercise files to make learning more meaningful and much easier to absorb.

автор: Pedro J G P

•14 авг. 2020 г.

I always had the interest in learning about ML topics in general, because of the unlimited capabilities of powerful algorithms that make possible the processing of data that, for us humans, results generally in something non-comprehensible, but it brings out marvelous results from scratch! This course offered me not only the opportunity to increase my abilities with matrices development in OCTAVE/MATLAB (and many, but more many ways in that it is possible to do calculations with a focus oriented to a tremendous performance), but in essence and most relevant, the right path to learn something of so much utility and admiration (and to be honest, very complicated at beginning to get familiar with) in such a high-skilled professional manner for explaining all involved themes. Every scenario must have a context in order to be understood, and this course accomplish with it at utmost level. I assure you will be very satisfied with these lessons :)

автор: Rahul R

•20 янв. 2021 г.

This is an amazing course for anyone looking to get into ML & DL.

The course doesn't jump into code directly; first it covers all the basics that you will need for completing the coding exercises. Then, there are small quizzes every once in while. These quizzes help you to evaluate your understanding. After this, at the end of each week, you will have a programming assignment that helps you reiterate what you have learnt.

Once you complete this course, you will have all the knowledge required to learn more about ML & DL on your own from other courses/online.

I am a software engineer looking to change my area of domain, and this course was the one that I chose to complete first. After this, I am going to complete Deep Learning Specialization by deeplearning.ai, and then, a course or two in computer vision. My decision to take this course was absolutely right. Thus, if you really want to get into ML, I suggest that you take this course first!

автор: Simon C T

•16 июля 2020 г.

Firstly, I will like to say THANK YOU! to Mr. Andrew Ng. For putting in so much work and effort into such an amazing online program that provided me on a great introduced into Machine Learning.

I am passionate on learning and understanding first principles is something that I value. Knowing how certain concepts are formulated and developed helps me to visualize how things work. During this course Mr. Andrew Ng made complex technique very straightforward and comprehensible. This comes not only from having very good knowledge and experience but most importantly the ability to communicate to his students. Thank You Andrew Ng. for conducting this EXCELLENT course. I intend to continue to use and review the material and videos from this course as I develop my knowledge in Machine Learning. Truly loved the videos, especially the very last one and not because it didn't have a quiz in it .. :)

Simon Chan Tack (Machine Learning Engineer & student).

автор: Bruce M

•1 янв. 2017 г.

Great content and explanations. Exercises guided hands-on efforts through all of the major algorithms and concepts, including very useful tools such as training rate analysis, cost and gradient descent verification, etc. Exercises were well structured to focus on key concepts - i.e. didn't have to spend a lot of time sweating the details of loading datasets, plotting results, ... The code I take away is an excellent foundation with which to explore other datasets with confidence.

I was originally a bit dubious about Octave-based exercises based on some reviews I'd read from others (who were more R or python-centric), but I found it to be an excellent choice given the linear algebra underpinnings of all of the algorithms and exercises.

The linear algebra foundation and vectorization implementations I think better prepare me in approaching large scale ML problems leveraging existing / emerging linear algebra and ML libraries.

Thanks again!

автор: Bill F

•28 мая 2019 г.

Before taking this course, I took the Coursera Applied Data Science specialization which included a higher level view of ML and applications to NLP and Social Networks. That series did not delve into ML algorithms but focused on the application of ML libraries and understanding the some of the different learning classifiers and regressors. This course is an excellent complement to that specialization, the former gave a broader view of the landscape, and having that training, it gave me much greater understanding of and appreciation for what these algorithms were doing. It stretched my non-software background but I was able to learn enough to effectively complete the programming assignments. I know the whole machine and deep learning field is complex and challenging, and for a novice like me this is not going to turn me into an expert (as Prof Ng suggested) overnight, but I feel like this gave me a solid footing to further pursue it.

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