About this course
Course structureTeachers open the door. You enter by yourself. (Chinese proverb) This is structured as a five week course:
- Week 1: A little bit of everything
- Week 2: Evaluation
- Week 3: Simple classifiers
- Week 4: More classifiers
- Week 5: Putting it all together
- 5-10 minute video
- Quiz. But no ordinary quiz! In order to answer the questions you have to undertake some practical data mining task. You don’t learn by watching someone talk; you learn by actually doing things! The quizzes give you an opportunity to do a lot of data mining.
- Mid-class test at the end of Week 2
- Post-class test at the end of Week 5
This week …In Week 1 you will get started with data mining. You will install Weka, explore its interface, explore some data sets, build a classifier, interpret the output, use filters, and visualize data sets. At the end of the week you will know what it’s like to do data mining!
- Lead educator, Ian Witten
Want to keep
The University of Waikato online course,
Data Mining with Weka
- Logistics, David Nichols
- Video editing, Peter Oliver and Louise Hutt
- Captions, Jennifer Whisler
- Music: Mozart’s Divertimento No. 2, Allegro, performed by Woodside Clarinets: Paul King, Sarah Shieff and Ian Witten
- Share what you are learning, including difficulties, problems and solutions, with others in the class in a weekly discussion focused on the Big Question of the week and what you have learned
- Other discussions from time to time
- Transcripts are supplied for all videos
- Slides for all videos can be downloaded as a PDF file
Software requirementsYou will download and install the free Weka software during Week 1. 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.)
Prerequisite knowledgeYou need no programming experience for this course. And no math, though some high-school statistical concepts are used (means and variances, maybe confidence intervals).
Data Mining with Weka
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