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Data Mining Practical Machine Learning tools and techniques

By: Material type: TextTextPublication details: Amsterdam Morgan Kaufmann 2017Edition: 4thDescription: 621p. ill. 18 by 22.5cmISBN:
  • 9780128042915
LOC classification:
  • HD30.2 .D38 2017
Contents:
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
Summary: This work offers a grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books KMTC:MOLO CAMPUS Reference HD30.2 .D38 2017 (Browse shelf(Opens below)) 1 Available MLO/415

Previous 3rd edition:2011

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

This work offers a grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations

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