Pt. I. Machine learning tools and techniques -- 1. What's it all about? -- 2. Input : concepts, instances, and attributes -- 3. Output : knowledge representation -- 4. Algorithms : the basic methods -- 5. Credibility : evaluating what's been learned --
6. Implementations : real machine learning schemes -- 7. Transformations : engineering the input and output -- 8. Moving on : extensions and applications -- Pt. II. The Weka machine learning workbench -- 9. Introduction to Weka -- 10. The Explorer -- 11. The knowledge flow interface -- 12. The experimenter -- 13. The command-line interface -- 14. Embedded machine learning -- 15. Writing new learning schemes.
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