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Machine Learning, Stanford University

4.9
91,275 ratings
23,251 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 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.

By PT

Sep 01, 2018

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

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22,390 Reviews

By DONG ZHANG

Jan 24, 2019

Wonderful course! Thank you Ng!

By D Kothandaraman

Jan 24, 2019

very nice to learning ML

By miaomiaodewanzi

Jan 24, 2019

Thank you for your excellent lessons on Machine Learning, Dr. Ng! I really appreciate for your teaching and help!

By shinnosukeJ

Jan 24, 2019

It's very useful and i learn a lot from it

By Ian Finneran

Jan 24, 2019

Excellent class! I have taken many undergrad and grad STEM classes at university and this was one of the best. Easy to follow, taught in an interesting way, and you learn a ton in a short amount of time.

By 任盼飞

Jan 24, 2019

very good

By Josh Fry

Jan 24, 2019

Lectures were clear and helpful, and I now feel I have at least a basic grasp on the fundamentals of machine learning. For me the programming assignments were quite easy once I got comfortable with Matlab. You do learn how to vectorize your code, which is I think one of the most useful things I took away from this course. I kind of wish the exercise instructions didn't give away most of the details of the problems--for many of them you can just copy the equations given in the instructions without much thought.

By 刘承涛

Jan 24, 2019

The course system introduces the basics of machine learning and it is very beneficial to me.

By Devashish Mahato

Jan 23, 2019

very good course to understand concepts of machine learning in a much simpler way .

By ARTURO SIERRA

Jan 23, 2019

Fantastic. Andrew is an extraordinary instructor.