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 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.

Jun 20, 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

篩選依據：

創建者 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.

創建者 OMKAR K D

•Mar 30, 2019

Explained well but I was not able to implement it on exercise.

So I switched to other webites .Now I am back here and realized the value of this course

創建者 Rafael L d C

•Jul 19, 2019

Amazing course. It gets deep into the content and now I feel I know at least the basics of Machine Learning. This is definitely going to help me on my job! Thanks Andrew and the mentors of the course!

創建者 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.

創建者 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.

創建者 Goulven G

•Jan 10, 2019

This course could be a nice introduction and overview of the Machine Learning field.

However, the video transcripts are TERRIBLE — do not attempt to find any traces of grammar in them ! After a while I figured there were lecture notes (seriously, why hiding them under Resources ??? some people don't want to or simply can't watch the videos), but some of them lack information needed for the quiz so for some sessions you still have to watch the videos or endure the transcripts anyway.

But MOST OF ALL, the course has an incredible number of (acknowledged) errors, sometimes critical for the programming assignments, and you have to dig into the forum and Resources Erratas to figure them. Given that this lecture has been online at least since 4 years and some people actually PAY FOR IT, I find this utterly disrespectful, hence my low rating.

Furthermore, note that the validation script for ex5 is too permissive : it accepts wrong linearRegCostFunction implementations, which makes the second part of the assignment quite painful to debug…

創建者 JImmy C

•May 19, 2019

I‘m a Chinese post-graduate student of Computer Sciense. This class is very useful to me because of it's amazing course videos and the well-designed programming exercises. It is really lucky to have this opportunity to find the course and to finish it. This class will be a footstone for further studying in AI field for anyone who just get started.

創建者 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.

創建者 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.

創建者 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.

創建者 Anton D

•Apr 24, 2019

Overall, this is a great course and I learned an enormous amount of information. The biggest issue I had was the disconnect between the course and the assignments/quizzes. Although they had help sections, because you couldn't ask direct questions about the algorithms/quizzes, if you had a problem, you were basically on your own. (At least that is what it felt like.) For example, if you missed a quiz question and couldn't figure out the answer, there seemed little recourse to find the actual answer. In a couple cases, I decided to just take the 80% on a quiz simply because I had no idea what the answer was.

創建者 Cesare C

•Jun 20, 2018

good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)

創建者 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.

創建者 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

創建者 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.

創建者 Mathew L

•Sep 25, 2015

This course is absolute garbage. You get no feedback on your quizzes or assignments and the professor is one of the most boring I've ever seen. It's absurdly frustrating to repeatedly fail without any feedback as to why you're failing.

The lectures are clearly from a math perspective, as the prof simply draws what he's talking about on the slides. His hand writing is poor, and he does a lackluster job of explaining what exactly he's doing.

Finally, pure lecture with no notes is almost impossible to learn, as there's nothing to read and study.

I'd rate this course a 1/10, take the course on iTunes from Caltech instead.

創建者 Rui C

•Dec 12, 2015

However good the material and lectures may be, the use of an outdated version of Octave (which is not Mac-friendly and exceedingly brittle, to the extent where the supplied code requires manual patching in Windows and Linux) is a complete turn-off and makes it nearly impossible to complete the assignments on time unless you're prepared to spend at least twice as much time debugging your setup as doing the actual assignments.

I'll come back when this is done with R or Python.

創建者 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!

創建者 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

創建者 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.

創建者 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.

創建者 Carlos E R d S

•Jul 16, 2019

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).

Andrew Ng is excellent

創建者 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.

創建者 Hou Z

•May 05, 2019

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

創建者 Nikhil J

•May 18, 2019

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