• Waikato

Advanced Data Mining with Weka

Learn how to use popular packages that extend Weka's functionality and areas of application. Use them to mine your own data!

12,458 enrolled on this course

Advanced Data Mining with Weka
  • Duration5 weeks
  • Weekly study4 hours
  • LearnFree
  • Extra BenefitsFrom $84Find out more
This course is part of the Practical Data Mining program, which will enable you to become a data mining expert through three short courses.

Extend your repertoire of data mining scenarios and techniques

This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualisation and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.

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Skip to 0 minutes and 5 seconds Hi! I’m Ian Witten from the beautiful University of Waikato here in New Zealand, and I want

Skip to 0 minutes and 11 seconds to tell you about our new online course: Advanced Data Mining with Weka. If you liked the other courses–Data Mining with Weka and More Data Mining with Weka–you’ll love this new course. It’s the same format, the same software, the same learning by doing, and the aim is the

Skip to 0 minutes and 26 seconds same, too: to show you how to use powerful techniques of data mining on your own data. One difference is that the lessons in this course are given by different people. In fact, you’ll get to meet pretty well the whole Weka team. This new course is advanced. We’re going to be looking at new kinds of data. We’re going to be looking at time series, for example, where the data evolves over time and your job is to predict the future. Or situations where the characteristic of the source changes slowly over time, like it does in real life, and your job is to track those changes. We’ll look at different ways of working with big data.

Skip to 1 minute and 4 seconds We’re going to introduce you to Weka’s big sister, Moa, which is a stream-oriented data mining system that never stores the data in main memory, so it can operate on effectively infinite streams of data. We’ll also show you how to deploy Weka on a cluster computing environment using the Apache Spark framework, and also the popular Hadoop framework. We’re going to show you how you can reach out to other data mining systems from Weka, for example, the popular R data mining system. You can get at all the algorithms in R. We’ll look at scripting Weka in Python, and you can write little Python scripts right there in the Weka interface. By popular demand, we’ve included some applications. So that’s it.

Skip to 1 minute and 52 seconds Advanced Data Mining with Weka, coming soon to a computer near you. Hope to see you there. Bye for now!

What topics will you cover?

  • Time series analysis
  • Data stream mining
  • Incremental classifiers
  • Evolving data streams
  • Support vector machines
  • Accessing data mining in R
  • Distributed data mining
  • Map-reduce framework
  • Scripting data mining in Python and Groovy
  • Applications: Soil analysis, Sentiment analysis, Bioinformatics, MRI neuroimaging, Image classification

When would you like to start?

Start straight away and learn at your own pace. If the course hasn’t started yet you’ll see the future date listed below.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Discuss the use of lagged variables in time series forecasting
  • Explore the use of overlay data in time series forecasting
  • Identify several different applications of data mining with Weka
  • Compare incremental and non-incremental implementations of classifiers
  • Evaluate the performance of classifiers under conditions of concept drift
  • Classify tweets using various techniques
  • Calculate optimal parameter values for non-linear support vector machines
  • Demonstrate the use of R classifiers in Weka
  • Develop R commands and R scripts from Weka
  • Explain how distributed Weka runs Weka on a cluster of machines
  • Experiment with distributed implementations of Weka classifiers and clusterers
  • Explain how “map” and “reduce” tasks are used to distribute Weka
  • Design Python and Groovy scripts for Weka operations
  • Apply Python libraries to produce sophisticated visualizations of Weka output
  • Describe how Weka can be invoked from within a Python environment

Who is the course for?

This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions.

What software or tools do you need?

Before the course starts, download the free Weka software. It runs on any computer, under Windows, Linux, or Mac. It has been downloaded millions of times and is being used all around the world.

(Note: Depending on your computer and system version, you may need admin access to install Weka.)

What do people say about this course?

Interesting and well presented course. I'm now going to spend some more time comparing weka and R output and results from linear and logistical regression. Thanks for all the work that went into developing and offering this course.

WJ Kinder

I took my time - busy schedule these days - but finally completed the course! Thank you for the excellent teaching, for the friendly tone, and for the tremendous work which I am sure went behind putting this course together. I am looking forward to starting the next one, both to learn more about the selection of attributes and to learn more about text processing (that is a wonderful surprise for a neurolinguist like myself!). All the best!

Vania de Aguiar

Who will you learn with?

I grew up in Ireland, studied at Cambridge, and taught computer science at the Universities of Essex in England and Calgary in Canada before moving to paradise (aka New Zealand) 25 years ago.

Who developed the course?

The University of Waikato

Sitting among the top 3% of universities world-wide, The University of Waikato prepares students to think critically and to show initiative in their learning.

  • Established1964
  • LocationWaikato, New Zealand
  • World rankingTop 380Source: QS World University Rankings 2021

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