Pt. I. Machine learning tools and techniques. What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Transformations : engineering the input and output -- Moving on : extensions and applications -- Pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes.