Created by:  Stanford University

  • Andrew Ng

    Taught by:  Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Language
English, Subtitles: Spanish, Hindi, Japanese, Chinese (Simplified)
How To PassPass all graded assignments to complete the course.
User Ratings
4.9 stars
Average User Rating 4.9See what learners said
Syllabus

FAQs
How It Works
Coursework
Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers
Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates
Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators
Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Ratings and Reviews
Rated 4.9 out of 5 of 38,619 ratings

Great course

great opportunity for newbies in ML to get into the whole world of machine learning

extremely awesome!

As an introductory course into machine learning, this was an *excellent* course. I would HIGHLY recommend it. The videos and the accompanying programming exercises went very well together; and the free license to Matlab for this course was great (thanks, MathWorks)!

A few downsides: some of the programming exercises (esp. the latter ones) were quite trivial, and should have gone deeper. Much of the code was provided for the user. For example, the SVM lab focused on very basic programming that only required VERY few lines of vectorized code, while completely missing opportunities like exploring tuning the performance by deciding how many features to down select to.

While Dr. Ng suggested that students who have completed the course are now "experts" in machine learning (an encouraging statement, but far from reality), it would be more constructive to recommend next courses and/or books to read.

Lastly, it would have been REALLY REALLY nice if Dr. Ng had written a book with all of this course content. Other books I've read just don't have the same breadth, content & guidance. The slides/notes simply do not capture all of the material that he shared; and, it's not convenient to rewatch the videos as a replacement for a book. Walking away from this course I don't have any effective resources to turn to in order to revisit the material that was presented.