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Data Mining with Weka and More Data Mining with Weka are underway!

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Topics covered in Data Mining with Weka:

  • What is data mining?
  • Where can it be applied?
  • How do simple classification algorithms work?
  • What are their strengths and weaknesses?
  • In what ways are real-life classification methods more complex?
  • How should you evaluate a classifier’s performance?
  • What is “overfitting” and how can you combat it?
  • How can ensemble techniques combine the result of different algorithms?
  • What ethical considerations arise when mining data?

Topics covered in More Data Mining with Weka:

  • Running large-scale data mining experiments
  • Constructing and executing knowledge flows
  • Processing very large datasets
  • Analyzing collections of textual documents
  • Mining association rules
  • Preprocessing data using a range of filters
  • Automatic methods of attribute selection
  • Clustering data
  • Taking account of different decision costs
  • Producing learning curves
  • Optimizing learning parameters in data mining


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