Machine Learning

Andrew Ng, Associate Professor, Stanford University

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Machine Learning is now available in Coursera’s on demand format! To watch videos and complete assignments at your own pace, join the on demand course now at: https://www.coursera.org/learn/machine-learning/ 

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.

FAQ

  • What is the format of the class?

    The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments. This course is available in a self paced format here: https://www.coursera.org/learn/machine-learning

  • How much programming background is needed for the course?

    The course includes programming assignments and some programming background will be helpful.

  • Do I need to buy a textbook for the course?

    No, it is self-contained.

  • Will I get a statement of accomplishment after completing this class?

    Yes, participants who successfully complete the session-based class will receive a Statement of Accomplishment signed by the instructor.  Statements of Accomplishment are not available for the on demand version of this course.

Dates:
  • 19 January 2015, 10 weeks
  • 22 September 2014, 10 weeks
  • 16 June 2014, 10 weeks
  • 3 March 2014, 12 weeks
  • 14 October 2013, 10 weeks
  • 22 April 2013, 10 weeks
  • 20 August 2012, 10 weeks
  • 23 April 2012, 10 weeks
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
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  • Email-course:
  • Language: English Gb

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