Hi! I'm Ian Witten from the beautiful University of Waikato in New Zealand,
and I'd like to tell you about our new online course More Data Mining with Weka.
It's an advanced version of Data Mining with Weka,
and if you liked that,
you'll love the new course.
It's the same format, the same software,
the same learning by doing.
The aim is the same, as well, to enable you to use advanced techniques of data mining
to process your own data and understand what you're doing.
You don't need to have actually completed the old course in order to embark on the new one,
but we won't be covering things again,
so you will need to know something about data mining and the Weka machine learning workbench.
The course has short, 5-10 minute video lessons.
Slides and captions are available, as well,
along with optional readings from the data mining text book that the publisher
has agreed to make available for free.
There is a mid-course assessment and an end-of-course assessment, and if you do well in these,
you'll get a signed Statement of Completion from the University of Waikato.
As before, Weka will be a laboratory for you to learn the practice and the principles of
advanced data mining.
Each lesson is followed by a carefully designed activity that reinforces what you learned
in the lesson.
You're going to do most of your learning actually doing the activities.
You won't learn by listening to me talking or watching me do things, you'll learn by
doing stuff yourself.
There won't be any programming in this course, but a little bit of high school mathematics
might come in handy.
The topics were suggested by students who completed the early course.
We're going to start by looking at how to set up large scale experiments to compare
different learning and filtering techniques on your own data and different versions of
the dataset.
Then you'll get to experience big data.
You'll be working with datasets containing many millions of instances,
and I'll show you how to use Weka to process even larger,
effectively unlimited datasets, as well.
Then we'll look at document classification.
Text mining is a very popular application of machine learning.
We'll look at association rules and clustering.
We'll look at attribute selection and how to use cost models of your problem
to optimize the cost.
You'll even get to set up your own neural network in the Weka toolkit.
Of course, you'll need a computer,
because you'll be installing Weka on your own machine.
You need an internet connection,
and you'll need a Google account,
because we're using the Google infrastructure again for the MOOC.
You'll need plenty of motivation, and you'll need a bit of time, as well.
A few hours each week for the 5-week duration of the course.
You should allow a little bit more for this course than the previous one,
because the activities are more advanced and a little bit more demanding.
By the way, we've got new music, as well.
We're playing something written by a friend of mine.
You'll love it.
In fact, the music alone I think is worth the price of admission,
which is zero,
by the way.
This course is completely free.
More Data Mining with Weka,
coming soon to a computer near you!
Hope to see you there!