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Hi! I'm Ian Witten from the beautiful University
of Waikato in New Zealand,

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and I'd like to tell you about our new online
course More Data Mining with Weka.

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It's an advanced version of Data Mining with Weka,

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and if you liked that,

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you'll love the new course.

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It's the same format, the same software,

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the same learning by doing.

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The aim is the same, as well, to enable you
to use advanced techniques of data mining

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to process your own data 
and understand what you're doing.

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You don't need to have actually completed
the old course in order to embark on the new one,

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but we won't be covering things again,

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so you will need to know something about data
mining and the Weka machine learning workbench.

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The course has short, 5-10 minute video lessons.

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Slides and captions are available, as well,

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along with optional readings from
the data mining text book that the publisher

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has agreed to make available for free.

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There is a mid-course assessment and an end-of-course
assessment, and if you do well in these, 

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you'll get a signed Statement of Completion from
the University of Waikato.

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As before, Weka will be a laboratory for you
to learn the practice and the principles of

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advanced data mining.

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Each lesson is followed by a carefully designed
activity that reinforces what you learned

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in the lesson.

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You're going to do most of your learning actually
doing the activities.

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You won't learn by listening to me talking
or watching me do things, you'll learn by

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doing stuff yourself.

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There won't be any programming in this course,
but a little bit of high school mathematics

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might come in handy.

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The topics were suggested by students who
completed the early course.

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We're going to start by looking at how to
set up large scale experiments to compare

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different learning and filtering techniques
on your own data and different versions of

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the dataset.

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Then you'll get to experience big data.

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You'll be working with datasets containing
many millions of instances,

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and I'll show you how to use Weka to process
even larger,

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effectively unlimited datasets, as well.

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Then we'll look at document classification.

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Text mining is a very popular application
of machine learning.

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We'll look at association rules and clustering.

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We'll look at attribute selection and how
to use cost models of your problem 

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to optimize the cost.

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You'll even get to set up your own neural
network in the Weka toolkit.

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Of course, you'll need a computer,

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because you'll be installing Weka on your
own machine.

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You need an internet connection,

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and you'll need a Google account,

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because we're using the Google infrastructure
again for the MOOC.

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You'll need plenty of motivation, and you'll
need a bit of time, as well.

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A few hours each week for the 5-week duration
of the course.

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You should allow a little bit more for this
course than the previous one,

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because the activities are more advanced and
a little bit more demanding.

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By the way, we've got new music, as well.

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We're playing something written by a friend of mine.

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You'll love it.

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In fact, the music alone I think is worth
the price of admission,

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which is zero,

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by the way.

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This course is completely free.

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More Data Mining with Weka,

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coming soon to a computer near you!

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Hope to see you there!