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Оценки: 129,157

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Рецензии: 31,847

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....

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.

Feb 20, 2016

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.

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автор: Fernando A H G

•Jul 21, 2019

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: Prakash M

•Jul 14, 2019

This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

автор: Eric J

•Mar 27, 2018

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.

автор: Prateek J

•Jan 21, 2019

Exceptional. Best course to start learning Machine Learning! Only one grouse though, the exercises are in Matlab and not in python.

автор: Hou Z

•May 05, 2019

Very good instruction for machine learning, and also very very good for new comers!!!

автор: Nikhil J

•May 18, 2019

It was a great learning experience. All the lectures were in details.

автор: Aditya K

•May 18, 2019

It was a very helpful course.

автор: Prabhu N

•May 28, 2019

Course content was awesome, gave me lot of insights. If assignments were in Python, it would have helped a lot to improve my skills. Anyways I would recommend this course to a beginner who wants to understand the logic behind the machine learning process. Thank You AndrewNg Sir!!!

автор: Abdul Q

•Mar 03, 2018

An amazing skills of teaching and very well structured course for people start to learn to the machine learning. The assignments are very good for understanding the practical side of machine learning.

автор: Anup B D

•Apr 22, 2017

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: Natasha

•Oct 15, 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

автор: Kothala M K

•May 18, 2019

Good Course

автор: Marcin K

•Mar 02, 2018

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: Harshal M

•Mar 25, 2019

If this course was in python or R it would have been easier to understand. Octave/MATLAB is not that widely used.

автор: Sergey K

•Jan 24, 2016

Level of difficulty of lectures is not correspond with level of quizzes. In lectures they are talking about simple stuff and then in quizzes they ask you about details they didn't mentioned. You could deducts this information though. But this is exactly the main problem with this course - for quizzes you should deduct and learn by yourself so much stuff, that videos start to be not worth your time.

автор: Rune F

•Dec 18, 2016

Fairly good videos explaining the material, probably worth 4 starts. However, the written support material should be improved. IMHO the video should supplement the written material, i.e. it should be possible to learn the material only by reading. This is not the case, so frequent pausing of videos and making lots of notes is needed if one wants to commit this course to long-term memory.

автор: Herman v d V

•Jan 15, 2019

My first open online course from Stanford University gave me a lot of energy. As my student years are far behind me (I am 76 years old) it was a discovery to become enthusiast in this new area. And building on my career in ICT, this is a surprising extension on the way systems can help us to develop a better life. Professor Ng is very good in offering in a controlled way many insights in the machine learning - now it is time for me to apply my new knowledge!

автор: Brian L

•May 25, 2019

There's one saying in Chinese that says "一日為師，終身為師" which means once being someone's teacher, even just one day, you're the teacher for the rest of his life. Thank you for all your efforts and I really appreciate it. I'll keep working on Machine Learning and hopefully one day I can do the same contribution to the human society as you did.

автор: vinod

•May 18, 2019

Explanation was very good and assignment helps us to understand the real picture. The way course is planned along with octave exercise, Graphs and visualization of data (X,Y) is very good. Very good course who is starting the Machine learning from scratch.

автор: Quoc-Viet P

•Jun 25, 2018

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

автор: Rajdeep D

•Mar 31, 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

автор: anand

•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.

автор: Marius N

•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.

автор: Shweta K

•Oct 26, 2017

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

автор: Karl M

•Aug 11, 2017

Very nicely explained the mathematical topics, even for people like me with some phobia regarding large formulas. Useful hands-on experience with MATLAB coding, which I would have had to learn anyway.

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