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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....

SS

16 мая 2019 г.

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

EJ

26 мар. 2018 г.

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

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автор: Rishav S

•18 янв. 2019 г.

It would be better if it would have been done in Python

автор: Robert G C J

•10 авг. 2018 г.

Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. A few minor comments: some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm. I would have preferred to have worked through more of the code. Also, there were a few times when the slides didn't contain the complete equations so it was difficult to piece it all together when writing the code. Lastly, I wish that there was more coverage on vectorized solutions for the algorithms.

автор: Deleted A

•18 мар. 2017 г.

This is an extremely basic course. Machine learning is built on mathematics, yet this course treats mathematics as a mysterious monster to be avoided at all costs, which unfortunately left this student feeling frustrated and patronized. So much time is wasted in the videos with arduous explanations of trivialities, and so little taken up with the imparting of meaningful knowledge, that in the end I abandoned the videos altogether. The quizes were basic (largely based on recall of, rather than application of knowledge), as were the programming assignments (nearly all of which were spoon-fed, with the tasks sometimes being simple as multiplying two matrices together).

If you are serious about machine learning and comfortable with mathematics (e.g. elementary linear algebra and probability), do yourself a favour and take Geoff Hinton's Neural Networks course instead, which is far more interesting and doesn't shy away from serious explanations of the mathematics of the underlying models.

автор: Vasily

•7 апр. 2019 г.

I've never expected much from an online course, but this one is just Great!

Even if you feel like you have gaps in your calculus/linear algebra training don't be afraid to take it, because you'll be able to fill most of those right from the course material or at least figure out where to look.

This course gives grand picture on how ML stuff works without focusing much on the specific components like programming language/libraries/environment which most of ML courses/articles suffer from.

This leaves you with freedom to pick it yourself and apply gained knowledge however you want.

Biggest takeaway for me as a person working on my own project is amount of attention professor Ng brings to methods of evaluating your ML methods efficiency and how this correlates with time/effort you should put into the specific system component.

Because i feel like this is where most people slip up in practice.

Great thanks for all of that!

автор: anhhuy

•7 нояб. 2018 г.

I am Vietnamese who weak in English. To learn this course I have to choose playback rate 0.75.

But the teacher - Professor Andrew Ng talks clearly and the way he transfer knowledge is very simple, easy to understand. Myself is excited on every class and I think I am so lucky when I know coursera.

This course provide a lot of basic knowledge for anyone who don't know machine learning still learn.

Once again, I would like to say thank to Professor Andrew Ng and all Mentor.

(I hope all of you understand my feeling because of my low level English, I cannot express it exactly)

автор: Murali N

•14 июня 2016 г.

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

автор: Rishav K

•20 авг. 2019 г.

It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

автор: Pooritat T

•1 сент. 2018 г.

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

автор: Marcin K

•2 мар. 2018 г.

The course covers a lot of material, but in a kind-of chaotic manner. There is a lot of math, so if you're not familiar with linear algebra you may find it really difficult. Personally, I don't quite understand the approach. The goal of this course seems to be to teach people how the algorithms work, and if so - there is just enough math, for the students to get lost, but not enough of it to truly understand what's going on internally in the algorithms.

Also, the vectorization techniques of the provided formulas is not quite well explained, and it's left to the students to figure it out. This lead me a lot of times to trial and error approach, when I was just trying different approaches until something worked, but it was still hard for me to understand what really happened. Oftentimes I found myself spending more time on trying to understand how the matrices and vectors are being transformed, than actually thinking how the algorithm works and why.

Another thing is that after finishing the course, you have almost ZERO experience with real-world tools you're supposed to use for real-world projects. I'm thinking TensorFlow, R, Spark MLib, Amazon SageMaker, just to name a few.

On the bright side, the course teaches several general good practices like splitting the datasets to training, cv and test. It also explains very well how to work with different ML algorithms, how to monitor they are "learning well", and how to fine-tune their parameters or tweak the inputs, in order to gain better results.

The course ends with assuring students that their skills are "expert-level" and they are ready to do amazing things in Silicon Valley. That is obviously not true for the reasons I already mentioned (e.g. lack of tooling experience). I see this course as a starting point for anyone who seriously wants to go into ML topics, and to actually understand at least some of the internals of the 3rd party libraries he'll end up using. But don't think you'll end this course with any practical knowledge, or that you'll be ready for real-world problem solving.

автор: Имильбаев Р Р

•25 дек. 2018 г.

It would be ideal course if instead of octave pyhon or r is used

автор: Rajeev A

•8 мая 2019 г.

This course has of course (pun intended) built a formidable reputation for itself since it was laucnhed. I took the course in 2019 when it had been around for a few years and so what I am saying here may resonate with a lot of people who have taken the course before me. "Concretely"(!), Prof Ng takes the student on a very well structured journey that covers the vast canvas of ML, explaining not just the theoretical aspects but also laying equal empahsis on the pratical aspets like debugging or choosing the right approach to solving a ML problem or deciding what to do first / next. At that level this course is highly recomended by me as the first course in ML that anyone should take. I do have a suggestion to make regarding how some of the portions could have been explained more lucidly. These are portions that pertain entirely to the mathematics and programming problems, where I struggled for days and (for back propogation) for months before realising that maybe the explanation given in the slide wasn't clear enough and at times i just needed to try really random ideas to get out of the programmin rut that I was stuck in. An advise for anyone doing the course would be to write down the matrices in full detail and do the transformations of cost fucntion and gradient descent or back prop using pen and paper and attempt to write the code for it only after once one is clear about the exact mathematical operation happening. Thank you, Prof Ng for gifting this course to the online learners community and I would also like to thank the mentors who have replied to the queries patiently while stadfastly enforcing the honour code.

автор: Jason S

•17 июня 2017 г.

Everything is taught from basics, which makes this course very accessible- still requires effort, however will leave you with real confidence and understanding of subjects covered. Great teacher too..

автор: Bruno C

•9 нояб. 2015 г.

The course is ok but the certification procedure is a mess!

No statement of accomplishment and you have to retake all the assignments if you want the certificate and had not been verified ....

автор: Fadi

•14 апр. 2019 г.

I just started week 3 , I have to admit that It is a good course explaining the ideas and hypnosis of machine learning . The instructor takes your hand step by step and explain the idea very very well.

The thing is, there is no practical example and or how to apply the theory we just learned in real life.

This course in to understand the theories , not to apply them.

For someone like me ( far away from Algebra) it is really not for me. Despite i want to learn the applied ML

автор: Olga K

•18 апр. 2018 г.

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

автор: vamshi b

•2 окт. 2016 г.

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

автор: Harsh S

•3 мар. 2018 г.

My first and the most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course. Thanks Andrew Ng and Coursera for this amazing course.

автор: Mike L

•19 авг. 2017 г.

Very helpful and easy to learn. The quiz and programming assignments are well designed and very useful. Thank Prof. Andrew Ng and coursera and the ones who share their problems and ideas in the forum.

автор: David W

•20 февр. 2016 г.

Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.

автор: Rohit S

•13 авг. 2019 г.

Andrew Ng is a great teacher.

He inspired me to begin this new chapter in my life. I couldn't have done it without you

and also He made me a better and more thoughtful person.

Thank You! Sir.

автор: Miguel Á A S

•24 июля 2019 г.

This course is one of the most valuable courses I have ever done. Thank you very much to the teacher and to all those who have made it possible! I will recommend it to all those who may be interested.

автор: Fernando A H G

•21 июля 2019 г.

Exceptionally complete and outstanding summary of main learning algorithms used currently and globally in software industry. Professor with great charisma as well as patient and clear in his teaching.

автор: Marius N

•31 окт. 2017 г.

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.

автор: Anup B D

•21 апр. 2017 г.

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

автор: Rudi P

•19 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.

A big thank you for spending so many hours creating this course.

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