Hello,
and welcome to More Data Mining with Weka.
I'm Ian Witten,
and I'm presenting the videos for this course,
which is brought to you by the Computer Science Department at the University of Waikato in
New Zealand.
This course follows on from a previous course,
Data Mining with Weka.
It's a practical course on how to use the advanced facilities of Weka for data mining.
As in the previous course,
we're not going to cover programming,
just the interactive interfaces to Weka.
We're going to pick up some basic principles of data mining along the way.
We're assuming that you know about a number of things that you will have learned in Data
Mining with Weka: what data mining is and why it's useful,
all the motivation,
simplicity first,
using the Explorer interface,
popular classifier and filter algorithms,
evaluating the result,
interpreting the outputs,
avoiding the pitfalls of training and testing sets,
and the overall data mining process.
We're not going to cover any of that in this course.
If you want a refresher,
then you can go to YouTube and look at the WekaMOOC channel where you'll see all the
videos for the previous course.
As you know,
a weka is a bird found only in New Zealand,
but from our point of view,
it's a data mining workbench -- the Waikato Environment for Knowledge Analysis,
which contains a lot of machine learning algorithms.
A very large number of algorithms for data mining tasks: preprocessing algorithms,
feature selection,
clustering,
association rules -- things like that.
It's a pretty comprehensive machine learning workbench.
What you're going to learn in this course is how to use the other interfaces to Weka.
We already know how to use the Explorer,
but we're going to talk about the Experimenter,
the Knowledge Flow Interface,
and the Command Line interface.
We're going to talk about "big data" and how you deal with that in Weka.
We'll do some text mining.
We'll look at filtering using supervised and unsupervised filters.
We'll learn about discretization and sampling.
We'll learn about attribute selection.
We'll learn about classification rules,
rules vs. trees,
association rules,
clustering,
cost-sensitive evaluation and classification.
Most of all,
I'm trying to get you to a point where you can use Weka on your own data,
and -- most importantly -- understand what it is that you're doing.
This is the first class.
It's called "Exploring Weka's interfaces,
and working with big data." We're going to look at the Experimenter interface,
which is used for comparing different classifiers on different datasets.
We're going to look at the Knowledge Flow interface.
We're going to look at the Simple Command Line interface.
And we're going to talk about big data,
how to work with big data.
The Explorer works with pretty big data,
about to maybe a million instances with maybe 25 attributes each.
Using the Simple Command Line interface in the right way,
you can deal with much larger datasets,
effectively unlimited datasets or "data streams".
In the activity,
for example,
you'll process a multi-million instance dataset with 10 million instances.
It's pretty big stuff.
The course is organized just like the previous one with 5 classes.
Each class has got about 6 lessons.
The next class is on discretization and text classification.
Then we're going to look at classification rules,
association rules, and clustering.
Then we'll look at attribute selection and cost-sensitive classification.
Finally, in Class 5 we'll look at neural networks,
learning curves,
and performance optimization.
There are six lessons in this class.
Each lesson consists of a little video,
like this one.
Following each lesson is an activity,
which you're strongly encouraged to do.
You're going to learn by doing here,
by doing the activities,
not just by listening to me talk about them.
We very strongly encourage you to do the activities.
However, you don't have to do them.
You don't have to complete them all.
The assessment for the course is based on a Mid-class assessment and a Post-class assessment
worth 1/3rd and 2/3rds of the marks, respectively.
If you get more than 70% on these assessments then you'll get a Certificate of Completion
at the end of the course.
I'd like you to download Weka now.
I'd like you to download the new version of Weka,
which has appeared recently: 3.6.11.
That's not the one that was used for the previous course.
It's the latest stable version of Weka.
You know how to download it,
so I'm not going to talk about that at all.
Just do it!
We have the same text book as we had for the previous course,
and the same excerpts the publisher has kindly made available for free in e-book format.
Let me just finish off by saying this is where New Zealand is,
at the top of the world.
We think of you as being "down under",
not us as being "down under".
We're in the top center of the world.
Here in New Zealand -- actually,
I've turned this map around with North at the top,
which is probably what you're used to -- you can see where the University of Waikato is
pointed to by the red arrow.
That's where I am.
That's the end of the first lesson.
Please go and do the activity associated with this lesson,
which actually involves all of the assessment questions from the last course.
Go through and do these assessments again.
Well,
you don't *have* to do them,
but you should certainly look at them.
If you know your stuff,
it won't take you long to do them,
and if you don't know your stuff,
it's particularly valuable that you do them.
Go ahead and look at that now,
and I'll see you in the next lesson.
Bye for now!