This text introduces machine learning tools and techniques, covering input concepts, instances, and attributes, and output knowledge representation through algorithms. It outlines basic methods, including credibility evaluation, real-world implementations, data transformation, ensemble learning, handling massive data sets, and practical data mining. The Weka machine learning workbench is introduced, detailing its components such as the Explorer, Knowledge Flow Interface, Experimenter, and Command-Line Interface. It also discusses embedded machine learning and writing new learning schemes. The content is structured with a table of contents provided by Blackwell's Book Services and R.R. Bowker, used with permission. The text serves as an overview of key areas in machine learning, emphasizing both theoretical foundations and practical applications. It highlights the importance of evaluating learned knowledge, transforming data, and using various tools like Weka for implementing machine learning schemes. The discussion covers a range of topics from basic methods to advanced techniques, providing a comprehensive guide for understanding and applying machine learning in real-world scenarios.This text introduces machine learning tools and techniques, covering input concepts, instances, and attributes, and output knowledge representation through algorithms. It outlines basic methods, including credibility evaluation, real-world implementations, data transformation, ensemble learning, handling massive data sets, and practical data mining. The Weka machine learning workbench is introduced, detailing its components such as the Explorer, Knowledge Flow Interface, Experimenter, and Command-Line Interface. It also discusses embedded machine learning and writing new learning schemes. The content is structured with a table of contents provided by Blackwell's Book Services and R.R. Bowker, used with permission. The text serves as an overview of key areas in machine learning, emphasizing both theoretical foundations and practical applications. It highlights the importance of evaluating learned knowledge, transforming data, and using various tools like Weka for implementing machine learning schemes. The discussion covers a range of topics from basic methods to advanced techniques, providing a comprehensive guide for understanding and applying machine learning in real-world scenarios.