Data Mining: Practical Machine Learning Tools and Techniques (Paperback, 5th Edition)

PART I: INTRODUCTION TO DATA MINING 1. What’s it all about? 2. Input: concepts, instances, attributes 3. Output: knowledge representation 4. Algorithms: the basic methods 5. Credibility: evaluating what’s been learned 6. Preparation: data preprocessing and exploratory data analysis 7. Ethics: what are the impacts of what's been learned? PART II: MORE ADVANCED MACHINE LEARNING SCHEMES 8. Ensemble learning 9. Extending instance-based and linear models 10. Deep learning: fundamentals 11. Advanced deep learning methods 12. Beyond supervised and unsupervised learning 13. Probabilistic methods: fundamentals 14. Advanced probabilistic methods 15. Moving on: applications and their consequences