000			01265nam a22002177a 4500
003			KENaKMTC
005			20240129100951.0
008			231031b ||||| |||| 00| 0 eng d
020			_a9780128042915
040			_cDLC
050			_aHD30.2 _b.D38 2017
100			_aWitten Ian H.[et al.]
245			_aData Mining _bPractical Machine Learning tools and techniques
250			_a4th
260			_aAmsterdam _b Morgan Kaufmann _c2017
300			_a621p. _bill. _c18 by 22.5cm
500			_aPrevious 3rd edition:2011
505			_aPart 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
520			_aThis 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
942			_2lcc _cBK _xV.NGENO
999			_c32173 _d32173