Data Mining – A Practical Machine Leaning Tools and Techniques

About

This book presents the basic theory of automatically extracting models from data, and then validating those models. The book does an exceptional job of explaining the various models (decision trees, association rules, linear models, clustering, Bayes nets,neural nets) and how to apply them in practice. With this basis, they then walk through the steps and pitfalls of various approaches. They describe how to safely scrub datasets, how to build models, and how to evaluate a model’s predictive quality

Author

  • Ian H. Witten Department of Computer Science University of Waikato
  • Eibe Frank Department of Computer Science University of Waikato

What you will learn

  • Machine learning tools and techniques
  • Input: Concepts, instances, and attributes
  • Output: Knowledge representation
  • Algorithms: The basic methods
  • Credibility: Evaluating what’s been learned
  • Implementations: Real machine learning scheme
  • Transformations: Engineering the input and output
  • Extensions and applications
  • The Weka machine learning workbench
  •  The Knowledge Flow interface
  •  The Command Flow interface
  • Embedded machine learning
  • Writing new learning schemes

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