Class information

Provider Coursera logo
Institution Stanford University
Code N/A
Language English
Next session To be announced
Estimated workload 5-7 hours/week
Level N/A
Categories Computer Science
Computer Science: Artificial Intelligence
Statistics & Data Analysis
Instructors Andrew Ng

Sessions

Start date Duration
Past Jan. 19, 2015 10 weeks
Past Sept. 22, 2014 10 weeks
Past June 16, 2014 10 weeks
Past March 3, 2014 12 weeks
Past Oct. 14, 2013 10 weeks
Past April 22, 2013 10 weeks
Past Aug. 20, 2012 10 weeks
Past April 23, 2012 10 weeks

Note that archives might still be available for past sessions.

Description

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 ...

Read the full description on Coursera

Prerequisites

N/A

Blackboard

6 posts

  • Written 6 months, 1 week ago
    Materials: Poor
    Difficulty: Easy
    Dropped Out
    Too easy for me!
  • ericajay
    Written 2 years, 5 months ago
    Interested
    I wish to follow this course but it seems I am enrolling on the very last day of it. Do you plan to have a next session ? thank you.
  • Written 3 years, 7 months ago
    Materials: OK
    Difficulty: Intermediate
    Successfully Completed (with Certificate)
    I was a little disappointed with this course,
    It might be that I had high expeditions because I heard a lot of good about it.
    Don't misunderstand me this course is a very good
    first course on machine learning.
    The lectures, review questions and programming assignments is
    very good and easy. But the material have been simplified and details is missing compared to the Stanford lectures. A subject I find very useful when working with learning algorithm's is information theory but it was not mentioned with one word. A good course if you have never heard about linear/logistic regressions, neural networks and clustering but If you have seen it before there is not much insight and details to get in this course.
  • Written 3 years, 7 months ago
    Materials: Excellent
    Difficulty: Intermediate
    Successfully Completed (with Certificate)
    This was the first MOOC I took and have completed a number of them since. Some have been wonderful but Andrew's ML course still reigns supreme.
    Even though I took this course two years ago it is still fresh in my mind. Informative & Inspirational.
  • Written 3 years, 8 months ago
    Materials: Excellent
    Difficulty: Intermediate
    Successfully Completed
    One of the greatest!

    Professor Andrew Ng is great, he makes you understand and doesn't try to make you feel dumb, he explains it all that you need to use Machine Learning without overwhelming you with mathematical complexities.

    This course has many of the greatest Machine Learning algorithms that you can use to work in many many applications.
  • Written 3 years, 8 months agoFeatured
    Materials: Excellent
    Difficulty: Intermediate
    Successfully Completed (with Certificate)
    This is the best course I have taken - it is really well done and Andrew is a great teacher.
    You end up with practical skills on machine learning, and although the maths looks quite complex he takes the time to explain it well.


    I like the template idea for programming assignments, as that is not unlike what happens in the real world
    - great feedback system on the assignments (at each stage they build in tests that you can check to see how you are going before you have to submit the assignment)

    I would absolutely recommend this course to anyone interested in the subject, as although there is a lot of maths, you will end up with practical skills at the end (and Octave is pretty impressive tool to learn)

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