Data Mining Practical Machine Learning tools and techniques
Material type:
- 9780128042915
- HD30.2 .D38 2017
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
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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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