Data Mining Practical Machine Learning tools and techniques
Witten Ian H.[et al.]
Data Mining Practical Machine Learning tools and techniques - 4th - Amsterdam Morgan Kaufmann 2017 - 621p. ill. 18 by 22.5cm
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
9780128042915
HD30.2 / .D38 2017
Data Mining Practical Machine Learning tools and techniques - 4th - Amsterdam Morgan Kaufmann 2017 - 621p. ill. 18 by 22.5cm
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
9780128042915
HD30.2 / .D38 2017