Our course "Data Mining Techniques: Theory and Practice" is a compact two day introduction into the foundations of data mining and the software RapidMiner. The theoretical backgrounds of all presented data mining techniques will be discussed and explained. Due to a high number of practical exercises, the participants will be able to transfer the gained knowledge to own data mining problems and solve them quickly and easily. Therefore, this course is probably the quickest possible way of getting the necessary insight into the ideas of knowledge discovery in databases and also shows all necessary practical aspects.

Details

• Course ID: 1002
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: users, decision makers, developers, administrators
• Previous knowledge: basic knowledge of computer programs and mathematics
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course is a compact introduction into the theoretical foundations of data mining and into the most important practical aspects of data mining with the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Machine Learning: Decision Trees, Rule Learning, Neural Networks, Nearest Neighbors, Bayes Learning, Support Vector Machines including a discussion of the algorithms behind those techniques and of their theoretical properties
  • Meta Learning: Bagging and Boosting
  • Preprocessing: Automated Feature Selection and Generation (amongst others with genetic algorithms), Discretization, Normalization, Sampling
  • Validation: introduction into performance criteria, cross validation, bootstrapping
  • Visualization: high-dimensional data visualizations, ROC plots, Self-Organizing Maps (SOM)
  Extensive exercises on different data sets will be performed for all topics.

Prices

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