Set Reminder Save for later

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.

Get Details and Enroll Now

OpenCourser is an affiliate partner of Coursera.

Get a Reminder

Not ready to enroll yet? We'll send you an email reminder for this course

Send to:



Stanford University

Rating 4.9 based on 18,979 ratings
Length 12 weeks
Starts Feb 18 (9 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

Get a Reminder

Get an email reminder about this course

Send to:

What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

artificial intelligence in 52 reviews

It is a good start for anyone keen on machine learning, neural network or artificial intelligence.

花了将近半年时间终于学完了,做完了所有的习题和编程题,Ng老师讲的很好,课程设计也很赞。有时候遇到看不懂的地方,Ng老师就反复强调两点,这个不重要只要会用就行了:)。然后做选择题都能做对,这就给了我继续学习的信心和动力,编程题也设计的很好,有各种提示和帮助 This course allowed me to get valuable knowledge about how artificial intelligence work.

I would recommend this course to every one interested in machine learning or artificial intelligence to help build a super strong foundation.

I feel this is the best course for any one who is about to dive into the world of artificial intelligence good Really informational and interactive.

As part of the process, I learnt many machine learning algorithms/concepts which will help me growing in artificial intelligence space.

A wonderful introduction to the world of machine learning which ignited my interest towards artificial intelligence and the course revives through all great algorithms making it the perfect base for foundation of machine learning.

Recommend to everyone who wants to start your learning about Artificial Intelligence.

This is very good and helpful course for all students, who want to excel in ARTIFICIAL INTELLIGENCE.This course gives us a basic idea, and many concepts related to Machine Learning.

logistic regression in 44 reviews

Because I was already familiar with most of the methods in the beginning (linear and multiple regression, logistic regression), I could focus more on the machine learning perspective that the class brought to these methods.

The course offers an overview on linear regression, logistic regression, neural network, support vector machine, regularization, validation, and some unsupervised machine learning methods.

Every algorithm from linear regression, logistic regression to neural networks all rely on the same fundamental concept.

I've tried other approaches before, like diving head first into neural networks without a clue about other simpler algorithms like linear and logistic regression and just got confused despite having no trouble with the mathematics.

I am currently up to week 3 on Logistic Regression and loving every minute of it.

Topics covered: linear regression, logistic regression, neural networks, SVMs, K-Means clustering, anomaly detection, etc.

My only suggestion is that during the concluding video provide a better summary of how all of the different approaches fit together ( Deep learning, nueral networks, linear regression, logistic regression, etc.

Thank you Just Excellent, everything about this course is just fantastic, beginning with Prof.Andrew, passing with his passion for the subject and his motivation to really make you understand everyword he says, he is keen on delivering all this expertise and this alone is a fine quality, the course is well organized and the quizzes and programing assignements are to the point and are a very good exercise, I just felt the course needed 2 small videos one addresssing the differences between linear regression, logistic regression, SVMs and Neural Networks and another video exciting people by a small example of machine learning on self-driving cars (very small programming assignments to help excite people and give them an-overall idea)again Thanks to Prof.Andrew and all who helped me find my hobby and passion :D the best course for machine learning beginners excellent course Informative, concise, and practical.

anomaly detection in 16 reviews

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.

random forest in 16 reviews

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.

Could have updated with some more most used algorithms Naive Bayes, Decision Tree and Random forest.

If adding more machine learning algorithms such as decision trees/random forests should be better.

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.

I'd like to see more algorithms covered - naive bayes, decision trees, random forests, more examples on neural networks (RNN, CNN) Very help.

aprendizaje automático in 15 reviews

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.

Curso muy recomendable tanto para aprender sobre Aprendizaje Automático (Machine Learning) como para recordar conceptos que estaban algo olvidados.

Un gran curso que abarca el Aprendizaje Automático enfocando con claridad y esmero aspectos teóricos y prácticos.

:-) Bromas a parte este curso nos da una muy buena idea de todo el potencial que tienen las herramientas de aprendizaje automático.

Un curso excepcional para entender qué es el aprendizaje automático y cómo trabajar eficientemente con las diferentes herramientas y conocimientos.

Excelente introducción al aprendizaje automático 非常适合想入门的人,学完后就可以看一些稍微进阶的课程了 very well explained....have seen the first online course to be so understandable very good course.It is my first machine learning knowledge.Thank very much I've enjoyed doing this course a lot.

Estupenda introducción al aprendizaje automático Awesome class.

support vector machines in 13 reviews

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.

The course mostly focuses on supervised learning giving an intuitive journey throughout different core machine learning algorithms such as linear/logistic regression, neural networks, support vector machines, etc.

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.


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.

Write a review

Your opinion matters. Tell us what you think.



Stanford University

Rating 4.9 based on 18,979 ratings
Length 12 weeks
Starts Feb 18 (9 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

Similar Courses

Sorted by relevance

Like this course?

Here's what to do next:

  • Save this course for later
  • Get more details from the course provider
  • Enroll in this course
Enroll Now