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Machine Learning

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.

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Rating 4.9 based on 34,528 ratings
Length 12 weeks
Starts Jan 31 (88 weeks ago)
Cost $79
From Stanford University via Coursera
Instructor Andrew Ng
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Data Science Machine Learning

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What people are saying

financial aid

The most outstanding mentors of my life and i am not a person eligible to comment on him...Thank you Sir for everything and the financial aid allowance by coursera I think this course is still amazing and one can feel the effort that has been put into all the videos, the assignments and the slides.

In addition, thank Coursera for providing such a great course and financial aid to me.

I am unable to find financial aid application page .

Thanks to Coursera for providing me financial aid in taking up this course.

This course was best course especially that pca part .It was explained so well.It also gave me a small depth of how face detection works.Great work by Andrew sir he is a great faculty also I'm thanful to coursera for letting me to do this course using financial aid.It helped me alot .Trying to do more courses in future Excellent Professor 人工智能学习的入门经典,希望多获取点项目实例 A really good course for an individual who's interested both in math and implementation of machine learning models 我学过的最舒服的课程,很受用。很享受每一个习题,享受编程。 Thoroughly enjoyed learning various techniques and background math involved.

Firstly I would like to thank the Coursera for allowing me to take this class on Financial Aid!This course gave the perfect start to the world of Machine Learning!I would like to sincerely thank Andrew NG for making this course awesome !thanks a lot !

Thanks a lot for financial aid that was a great help.

I am thankfull to you for providing such a great course and giving Financial Aid to poor students.

at last thanks coursera for providing me financial aid .

Honor to be one of 2.6 million students in this course, and glad to get the certificate under the financial aid package.

A very helpful course for my academics thanks a lot for the financial aid It is an excellent course.

I would especially thank the Coursera team for providing me with financial aid for this course.

I am truly grateful for the financial aid that Coursera has provided me, and would like to thank the course instructor, Dr. Andrew NG very very much!!!

I would also like to thank Coursera for providing me financial aid for this, I am highly indebted to this act of kindness.

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random forest

The willingness to dive into linear algebra is an recommended prequisite.Course gives not only insight into different kind of Machine Learning model paradigma but also offers practival advice for implementation of ML systems.Course does not coffer decision tree or random forest etc classifiers, which might have been nice.All in all, I can wholeheartedly recommend taking this course.

Please include Tree Based Models like Decision Trees, Random Forests etc.

Awesome couse, I feel Decssion trees and random forest algos also should be included.

And even though this course does not touch on all of the significant ML methods (e.g., random forests), it definitely delves (a purposefully chosen verb, mind you) into perhaps the most significant.

Also, important/popular algorithms like Random Forest were not included.

Please add the random forest algorithm.

Absolutely the best introductory course to machine learning!Wish the course also covered decision trees, random forests and boosting algorithms.

Excellent course as it is, but still would appreciate the inclusion of topics such as Monte Carlo Tree Search and Random Forest search as those also seem to be used often enough in ML settings.

using caret, glm, random forest etc in R. One of the most impressive courses I ever had.

I also a little surprised that Random Forests and Decision tree based algos were not covered in the course.

Needs to include decision trees - random forests and xgboost - which have become popular since this course was produced.

tree based algorithms, random forest etc.

Great course for understand the basics and some of the tricks nice Please cover additional topics like deep learning , random forest, etc .

很好。。 Very good.Only one negative point : Random Forest is not here 部分阅读材料无中文,理解有难度 Sir Andrew Ng is the best professor I have ever come across in my life.

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recommender systems

I only wish there we more programming exercises for the areas on Neural networks, SVM and recommender systems.

The course has a nice progression from fundamentals, like linear regression, to more complex models, like recommender systems.

By learning how to actually write the code, I now feel I understand to a much deeper level how facial recognition, recommender systems and autonomous driving works.

I had difficulties with the programming exercises for forward and backward propagation as well as with recommender systems.

But for someone who can handle all the notations and math (mostly linear algebra and optimization algorithms), this course is an ideal introduction into the concepts of Machine Learning (Supervised, Unsupervised & Recommender Systems, as well as diagnostic tools).

Barring a few topics in the end such as recommender systems which I feel was a topic too complex but had to be brushed through, I genuinely enjoyed every other one.

Now, for example, when visiting web sites I view recommender systems with a much better understanding of what they are doing behind the scenes.

It covers both Supervised and Unsupervised learning as well as some more advanced topics (Recommender Systems, Large Scale Machine Learning).

Same thing about recommender systems and spam-filtering.

Inspiring me to pursue Introduction to Recommender Systems at Coursera.

The examples in lectures and programming assignments are from real world applications and provides a great feeling to know how some of the things I use actually work behind the scenes, like recommender systems for movies/products, image to text conversion systems, image compresionm market segmentation etc.

Specific real life example for Recommender Systems, Character Recognition and large scale machine learning.The various topics on "advice" by Professor Andrew Ng is invaluable.

Normally I don't write many reviews, but after learning in this course how useful recommender systems are I would feel bed not giving coursera more data :) Great course for the reasons many others already wrote about!

I got an appetite to learn more about many Machine Learning subjects like recommender systems.

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anomaly detection

I strongly believe this course is a must have if you're starting in neural network, but also great for any kind of machine learning problem like image recognition, data classifier and anomaly detection, function modeling and so on.

Neural Networks or Anomaly Detection).

K means and anomaly detection.Anomaly detection is explained very well plus a example is given and solved whereas in K-means he said that we will assign points to clusters and move clusters but he didn't perform calculations so it was difficult for me to do calculations for this topic.

Especially in the Anomaly Detection, the usage of PDF was very ambiguous.

I also learned what I've learned when I was in the university like anomaly detection and neural network.

Topics like supervised/unsupervised learning, Anomaly Detection, Computer Vision though harsh are intuitively introduced to learners and clearly explained.

This course is perfect,I enrolled in April, and finished in August.In the beginning, I don't think this course will help me a lot in machine learning, but now I think what I thought in the beginning is absolutely wrong, I learned very much in this course, including logistic regression, linear regression, regularization, bias and variance, anomaly detection, principal component analysis, clustering algorithm-keans, recommender system, multiclassification, support vector machine, kernel methods, gaussian distribution, stochastic gradient descent, map-reduce, artificial neural network and backpropagation octave programming etc.If you want to break into machine learning, I think this course will help you to do so.

fraud detection) via anomaly detection algorithms.

You will learn about and apply many concrete machine learning algorithms (neural networks, linear and logistic regression, anomaly detection and others).

computer vision and anomaly detection.

I wish I had more teacher like this when I was a student.I wish there would be a follow up with maybe even more tips for large scale machine learning and datasets where the positive class is only a small fraction of all the data available (not anomaly detection though).Thanks a lot Andrew !

Now off to do some simple applications here at work like spam filter and anomaly detection to start.

机器学习入门课程,Andrew对许多算法的原理做了直观性的介绍,布置的作业也对深入理解很好的促进;motivation和application的介绍非常棒,跟时代结合紧密,比如spam和anomaly detection;内层的数学没有较多的介绍,所以整个课程上下来不会很疲惫,时间长度比较合理;建议老师在结课时推荐一些更深入学习机器学习的进阶课程。最后非常感谢老师的教学,这门课让人受益良多 很好的入门课程,调理清晰 It really is a great class!

Typical algorithms such as supervised and unsupervised learning algorithms, support vector machine, PCS, Clustering, Anomaly Detection, and so on were taught and explained completely.

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support vector machines

Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

He takes you through behind-the-scenes of magic of regression, classification, clustering, neural networks, support vector machines in a very lucid manner.

Enjoyable, easy-to follow and yet quite detailed introduction to things like logistic regression, neural networks, support vector machines, or recommender systems.

Turns out machine learning is just math, and this course teaches the calculus & linear algebra concepts needed to understand linear regression, neural networks, support vector machines, K-means clustering, and more.

In that regard, I found the lectures on support vector machines sadly very confusing (I learned more by downloading Andrew Ng's lectures notes from his actual Stanford course).It's nevertheless a good introductory course and I would recommend it to anybody who wants to learn the basics of machine learning.

For example, for Support Vector Machines and backpropagation for neural networks.

Andrew was a very good teacher, taking time to explain fundamentals of linear regression, logistic regression, neural networks, support vector machines, case studies and more.

However, the treatment of SVM (Support Vector Machines) was a bit too light for my tastes.

Mind you, I'm biased because I was a math major and want to see proofs for everything, but I would have really liked to see more of the details behind support vector machines and neural networks.

This course is an opportunity to get acquainted with several machine learning techniques, including linear regression, logistic regression, Support Vector Machines (SVM), anomaly detection, non-supervised learning (clustering, K-means, etc), recommendation systems and very interesting discussions about batch/mini-batch versus stochastic learning and large-scale learning systems.

Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).

The course runs 10 weeks and covers a variety of topics and algorithms in machine learning including gradient descent, linear and logistic regression, neural networks, support vector machines, clustering, anomaly detection, recommender systems and general advice for applying machine learning techniques.

The Matlab/Octave programming exercises are well-designed, and for someone who didn't know Octave before, learning this was a bonus.Compared with Hinton's 2012 course, Ng's is better for beginners, covers slightly different topics (Support Vector Machines, but not Boltzmann Machines, Belief Nets, and Autoencoders), and makes a knowledge of calculus optional (though it's more interesting if you have this - especially chain rule).

The course broadly covers all of the major areas of machine learning -- linear and logistic regression, neural networks, support vector machines, clustering, dimensionality reduction and principal component analysis, anomaly detection, and recommender systems.

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silicon valley

As Andrew Ng says, after the course you will know more than most engineers in Silicon Valley.

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.

Although I would have preferred a little more focus on the theory/mathematics underlying the algorithms, as opposed to silicon valley application tricks.

i particularly liked you silicon valley insights!

He is also very encouraging with statements that "By now you know more than the average Silicon Valley Engineer"... Bottom line is this course helps new bies grasp and love machine learning This course filled in many gaps in my knowledge and was presented at just the right level.

Prof. Ng has prepared many materials, including application cases in Silicon Valley, to illustrate the essence of machine learning.

In addition to the subject matter itself, it is interesting to learn that these are some of the key methods used in Silicon Valley.If you have a strong mathematical background, some of the early lectures are too slow.

Several times throughout the course, Andrew mentioned that learning the material presented in the course would put you above most ML users in Silicon Valley.

Another thing that I found great is the industry reference made, and that the same technology is being used in the Silicon Valley.

Its also quite annoying, how the lecturer calls anyone with a basic understanding of a mathematical field an expert while simultaneously claiming that most of the people in the silicon valley are clueless.In summary, its a good course for high schoolers, but anyone with a little mathematical background should rather spend his time/money with an other course.

(I had sold all chickens I raised in the vallage, and purchased a flight ticket to silicon valley so as to find a job.

See you in Silicon valley> Nice experiences here to have learnt a lot of knowledge about machine learning.

I also took particular issue with Andrew Ng's smug remarks about how anyone who takes this course is now "an expert in machine learning" or that 3 weeks in you already know more than many people doing ML in silicon valley.

I'm literally in silicon valley and there are no ML jobs to be had for anyone who doesn't have a dedicated degree and/or at least 3 years experience with whatever software they are using.

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without getting bogged down

This course does a great job of surveying some of the most popular and useful machine learning algorithms and digs into the math enough to give good intuition without getting bogged down in the details.

I really enjoyed Andrew Ng's teaching style, and even though I have a fairly extensive background in math, I appreciated his emphasis on applying these techniques without getting bogged down in proofs, etc.

The difficulty often lies finding that boundary — the boundary where the complexity of a computation or a problem or a strategy can be abstracted out (with a black-box, or an analogy) and a student can make progress in thinking about the problem without getting bogged down.

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.

If you want to apply machine learning for your work or project without getting bogged down by the intricate details (though it does provide snippets of the technical terms which you can learn it yourself later), then this is the course for you.

Ng, through this courses, demystifies machine learning and imparts useful skills without getting bogged down by the mathematics - if you're math-shy.

It goes into an appropriate amount of detail without getting bogged down by esoteric mathematics.

It gets into the background maths without getting bogged down with it.

The course instructor hinted at a lot of the theory that underlay the topics without getting bogged down in the rigor and thus gave a very good pragmatic and intuitive introduction to the material.

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get your hands dirty

Very useful course to get your hands dirty with coding and understand how machine learning works!

Andrew is an excellent teacher and the course contains enough material to give you confidence to get your hands dirty with real ML problems and hold technical conversations with colleagues.

The lectures are informative and insightful, and the homework makes you get your hands dirty with the algorithms.

A good starting point for those with some basic quantitative backgrounds Awesome introductory course to get your hands dirty Excellent course, Professor Ng is a great talker and the subjects of the course are very catchy and useful.

R and Python might have great ML libraries, but if you wish to get your hands dirty with the engineering behind those algorithms, the build, that is - then Octave is great!.

In other words -- you won't get a chance to get your hands dirty during this course.

Great course to get your hands dirty in the pool of machine learning and its approach.

I have really enjoyed this course so far and look forward to using What's great about this course is that it not only teaches about the theoretical aspects of Machine Learning, but it also gives you a chance to get your hands dirty and apply what you learn on real life applications.

I'd still recommend however to get your hands dirty through kaggle learn and then move here.

They help you get your hands dirty and understand the details of the concepts without needing to spend time debugging long scripts.

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los ejercicios de programación

Me encantaron especialmente los ejercicios de programación, donde se pone en práctica y se ve funcionando todo lo visto en clase.The course provides ALL the required material to understand the concepts.

Los ejercicios de programación semanales han sido de gran ayuda para poder comprender algunos conceptos que no quedan claros durante los videos.

Los ejercicios de programación no son tan tan complicados, aunque si hay que echarle rato ahsta que te salen.

La calidad pedagógica de las clases virtuales en video y de las evaluaciones, y la exigencia de los ejercicios de programación y el apoyo de los tutoriales y casos de práctica son sin dudas una referencia de clase mundial para cualquier curso on-line (y también presencial).

Muy completo, se centran en hacer entender la teoría y lo complementan con la práctica a través de los ejercicios de programación Great learning experience...Wow course!!!

Esto es con relación a los ejercicios de programación, parecería que fueron escritos con diferentes indices.

El enfoque de las misma resulta muy práctico con los ejercicios de programación, y la ayuda brindada en los foros es impecable.

Acabo de terminar el curso fue un viaje fantástico en algo que en verdad no conocía, pues no tenía conocimientos de programación (a decir cero), en el primer ejercicio de programación dije hoo!, y comencé a buscar tutoriales de Octave en internet, demore 3 semanas en entregar el primer ejercicio, mientras avanzaba en el curso me instruía más en Octave, hasta lograr nivelar las semana y hoy terminar el curso.El profesor Ng Anderw, un excelente tutor, y los tutoriales de los trabajos de programación excelentes, Tom en los tutoriales presenta herramientas muy entendibles para realizar los ejercicios de programación, excelente todo, gracias a los mentores por su apoyo, los moleste bastante, felicidades por este gran curso.PD: Soy graduado de ingeniero industrial hace 32 años, tengo 57 años, y lo que había visto de programación fue, basic, cobol, pascal, fortran cuando estudiaba en la universidad.

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right balance between theory

Fast paced and the right balance between theory and useful applications.

A wonderfully well structured course with the right balance between theory, insights and examples of applications of the learning algorithms.

Very good course, solid structure and right balance between theory and practice.Recommended!

The materials prepared by Prof Andrew and his team strikes a right balance between theory and practice.

Very good introduction to Machine Learning with the right balance between Theory (Maths) & PracticeWhat could have been even more awesome: Use R for assignments, An Overview of the other main ML Algorithms (decision trees, rules...) and a Summary of conditions where to use or not each category of algorithmBig thanks Andrew!!

Excellent course Great course, useful content, right balance between theory and practical exercises, taught by Prof Andrew Ng who is exceptional not only in teaching effectively the topics covered in the course but also passing on to you his passion for machine learning.

I am really enjoying this course!Right balance between theory and programming!

Great tutorial exercise Very high level, skips over a lot of the harder math, but gives a good overview over most of the main algorithms and talks a lot about helpful methods to improve them/use your time wisely Just about the right balance between theory and application.

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de aprendizaje automático

Muy buen curso, me agradó mucho tomarlo y entender los diferentes aspectos a tener en cuenta en el desarrollo de aplicaciones de aprendizaje automático I'm sure I'm the millionth person to say this, but absolutely fantastic course.

Un curso de complejidad creciente que proporciona una visión inicial para entender los procesos bajo los que operan los sistemas de aprendizaje automático.

Thanks for this AmAzing course)) very interesting and helpful course , i really get a lot of knowledge and information during this coursebut if there exist a reference book it would be better Es uno primeros y mejores cursos de Aprendizaje Automático.

Se profundiza en los detalles de construcción de todos los algoritmos de aprendizaje automático pero dejando claro los objetivos y forma de trabajo de cada uno de ellos.

Un excelente curso para tomar las bases de aprendizaje automático Very Nice I love it.

The best course about machine learning excellent teacher <3 Thanks for giving me this opportunity <3 Excelente curso para saber elegir que sistemas de aprendizaje automático son más apropiados utilizar para un determinado conjunto de datos.

Great Very instructive and detailed course :) Te va explicando de una forma ordenada y detallada los conceptos matemáticos alrededor de las principales técnicas de aprendizaje automático: supervisado ("lineal" y clasificación), no-supervisado y sistema de recomendación.

Después, da una pincelada sobre tecnologías de aprendizaje automático más complejas que también están a la orden del día.

If you want to start fro basics this is the course where you start from Gran curso para los que empezamos en este mundo del machine learning Excelente curso para tener una introducción sólida a los algoritmos de aprendizaje automático así como aplicaciones reales en la industria Great introduction to machine learning even for non-engineers with only basic math knowledge.

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iniciarse en el mundo

Un curso genial, con un enfoque muy bien adaptado para iniciarse en el mundo del machine learning.

Curso muy bueno para iniciarse en el mundo del machine learning, incluso sin tener gran base de matematicas (aunque si es muy recomendable) This course is very good.

La profundidad justa para iniciarse en el mundo del ML pero suficientemente específico para poder afirmar que has aprendido cada uno de los aspectos necesarios.

Un buen curso para iniciarse en el mundo del Machine Learning, pero no para una especialización seria, para eso se requiere un curso más avanzado.

Thanks again :) Muy buen curso para iniciarse en el mundo de Machine Learning Excellent course.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k


Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k


Sorted by most helpful reviews first

Guest says:

Amazing. I learned regression (linear and logistic), k-means clustering, and principal components analysis in uni, but seeing it in an ML context reinforced the grasp I have over those topics. Neural networks were entirely new to me and by far one of the most interesting concepts I've learned from anywhere!

Guest says:

this is a very good introduction to machine learning, i have already used it on two projects about for image recognition and customer service logs from company server. if you google it, you can also find many machine learning libraries for python, C++, etc. but in my opinion you can use those more effectively if you know what is happening behind the scene.

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Rating 4.9 based on 34,528 ratings
Length 12 weeks
Starts Jan 31 (88 weeks ago)
Cost $79
From Stanford University via Coursera
Instructor Andrew Ng
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics Data Science Machine Learning

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