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Machine Learning, Stanford University

4.9
94,275 ratings
23,842 reviews

About this Course

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....

Top reviews

By EJ

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.

By MN

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.

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22,983 Reviews

By Vyacheslav Gorkunov

Feb 23, 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

By Mohammed Abrar Ahmed

Feb 23, 2019

It is well organized course that has bit to bit approach in maturing any newcomer to the world of Machine Learning. I am proud that i had chosen the right course to learn.

By Atishay Jain

Feb 23, 2019

awesome course to learn basic to advance ml

By Annie Chen

Feb 23, 2019

It is a great course for introducing me to the machine learning world. Thank you.

By Aemie Jariwala

Feb 23, 2019

It was really amazing to learn new topics . Thank you for this course

By Hirotsugu Nakai

Feb 23, 2019

Thank you very much for this course! This course was very helpful to get an overview of machine learning.

By Rajat Biswas

Feb 23, 2019

Nicely created contents like in video quizes and the way he taught is just so good.

By Zheng Yang

Feb 23, 2019

The course is very well structured for me, a student who has some understanding of machine learning but would like to get a systematic introduction of the subject.

The course strikes a balance between depth and breadth. The amount of math and equations are just right. Prof. Ng did a good job stimulating the students' curiosity to dive deeper. And for those who want to get practical and hands-on, this course contains enough tools for machine learning practitioners.

I would recommend this course to anyone who is interested in machine learning but do not know where to start.

By Frederick Elliott Koch

Feb 23, 2019

I think that this is a very good top level discussion of ML and provides a good foundation for further exploration in the topic. I believe that there would be some added benefit providing optional videos discussing the underlying mathematics and statistics or at least list some good resources for those wanting / needing more mathematical rigor.

By SatyaVarma Rudraraju

Feb 23, 2019

conceptually very nice