Content Data Mining: Practical Machine Learning Tools and Techniques (4th Edition),
    WekaPractice
    Agant:Tung Hua Book Co., Ltd. (Contact: wthsieh@tunghua.com.tw, Manager Sieh(0927=887330))
    Weka
  1. From: Machine Learning Group at the University of Waikato
    Free online courses on data mining with machine learning techniques in Weka

  2. What is the difference between data mining, statistics, machine learning and AI?
  3. Chapter 1. What’s it all about?
    What's the Weka?
    Downloading and installing Weka
  4. Chapter 2. Input: concepts, instances, attributes
    What do you know about the "input" of data mining?
  5. Chapter 3. Output: Knowledge representation
    What do you know about the "output" of data mining?
  6. Chapter 4. Algorithms: the basic methods
    What do you know about the "Algorithm" of data mining?
  7. Chapter 5. Credibility: Evaluating what’s been learned
    What do you evaluate the performance or efficience of one data mining algorithm
  8. Chapter 6. Trees and rules
    DecisionTree_jdwang
    9. How to constuct one of Decision Tree (DT)?
    (Shanon Entropy)(Information Gain)
    Weka with weather(YouTube)
    WEKA Data Sets (weather)
    WEKA Data Sets (contact-lens.arff)
    wine.data