Created by:   Stanford University

  • Andrew Ng

    Taught by:    Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera

English, Subtitles: Spanish, 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


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How It Works

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

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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 28,147 ratings

Very good introduction into the field of machine learning, its various algorithms and use cases. Only point of criticism would be that the level is sometimes a little lower than expected. The pace of the lecture could, in my opinion, be a little faster as I found myself skipping through the videos from time to time hopping over the repetitions. Having said that, it's perfect for everyone without a mathematics background!

The exercises were very good fun and very well managed through the available material, resources and the discussion forum. At the end of the course one - me anyway - wishes to go back to uni and start studying computer science.

I think this was a great course. It goes through high-level concepts as well as digs down into implementation of common algorithms. Professor Ng explains concepts well enough for a novice to easily follow. Would recommend this course to anyone looking for a primer on machine learning.

A great introductory course that is designed such that you don't need a mathematical background in order to understand the concepts. Due to its attempt to maximise accessibility, most concepts are not mathematically motivated, meaning that a person serious about pursuing this as a career will need to do a subsequent course that is more mathematically rigorous. However, it does a great job of building intuition about the algorithms and is a solid base to build on.