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  • Everybody talks about Data Mining and Big Data nowadays. Weka is a powerful, yet easy to use tool for machine learning and data mining. This course introduces you to practical data mining.

    A new session starts on 29th February 2016. The course will run in a self-paced unsupported mode until 15th April, and Statements of Completion will only be produced after that date.

    The course features:

    Subscribe to the Announcements forum for updates and reminders.

    Please read the Terms of Service and Participant Information Sheet before registering.

    Prof Ian H. Witten
    Department of Computer Science
    University of Waikato

    Screenshot of Weka Explorer

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  • This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms. Students will work with multimillion-instance datasets, classify text, experiment with clustering, association rules, neural networks, and much more.

    A new session starts on 29th February 2016. The course will run in a self-paced unsupported mode until 15th April, and Statements of Completion will only be produced after that date. Students should have completed Data Mining with Weka, or have equivalent knowledge of the subject.

    The course features:

    Subscribe to the Announcements forum for updates and reminders.

    Please read the Terms of Service and Participant Information Sheet before registering.

    Prof Ian H. Witten
    Department of Computer Science
    University of Waikato

    Screenshot of Weka Explorer

    Go to course

  • This course follows on from Data Mining with Weka and More Data Mining with Weka. It provides a deeper account of specialized data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms. Students will analyse time series data, mine data streams, use Weka to access other data mining packages including the popular R statistical computing language, script Weka in Python, and deploy it within a cluster computing framework. The course also includes case studies of applications such as classifying tweets, functional MRI data, image classification, and signal peptide prediction.

    Students should have completed Data Mining with Weka and More Data Mining with Weka, or have equivalent knowledge of the subject.

    The course features:

    • Weka's "package manager"
    • a detailed syllabus
    • CC-BY videos & slides
    • online assessment leading to a Statement of Completion (example)
    • English & Chinese captions on YouTube and Youku

    Subscribe to the Announcements forum for updates and reminders.

    Please read the Terms of Service and Participant Information Sheet before registering.

    The course is given by the data mining group in the Department of Computer Science, University of Waikato:

    • Ian Witten, Mark Hall, Peter Reutemann, Eibe Frank, Albert Bifet, Pamela Douglas, Geoff Holmes, Mike Mayo, Bernhard Pfahringer, Tony Smith.

    Reaching out from Weka to other systems

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The MOOC series, Data Mining with Weka, is brought to you by the Department of Computer Science at the University of Waikato, New Zealand.