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Data mining practical machine learning tools and techniques

By: Contributor(s): Material type: TextTextLanguage: English Language Publication details: Cambridge, MA : Morgan Kaufmann 2017Edition: 4th edDescription: xxxii, 621 p. some Colour 23 cmISBN:
  • 9780128042915
Subject(s): DDC classification:
  • 006.312  WIT
Item type: Lending Books
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Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available 112995
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available 112996
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available $ 79.54 112864
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available $ 79.54 112865
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available $ 79.54 112866
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.312 WIT (Browse shelf(Opens below)) Available $ 79.54 112867
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006.312 WIT Data mining 006.312 WIT Data mining 006.312 WIT Data mining 006.312 WIT Data mining 006.312 WIT Data mining 006.312 YEN Data mining : theories, algorithms, and examples 006.32 KOS Neural networks and fuzzy systems

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 Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond

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