Data mining practical machine learning tools and techniques
Material type:
TextLanguage: English Language Publication details: Cambridge, MA : Morgan Kaufmann 2017Edition: 4th edDescription: xxxii, 621 p. some Colour 23 cmISBN: - 9780128042915
- 006.312 WIT
Lending Books
| Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | 112995 | ||||
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | 112996 | ||||
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | $ 79.54 | 112864 | |||
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | $ 79.54 | 112865 | |||
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | $ 79.54 | 112866 | |||
|
Lending Books
|
Applied Sciences Library Lending Section | Lending Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | $ 79.54 | 112867 | |||
Sheduled Reference
|
Applied Sciences Library Reference Section | Reference Collection | 006.312 WIT (Browse shelf(Opens below)) | Available | $ 79.54 | 112868 |
Browsing Applied Sciences Library shelves, Shelving location: Reference Section, Collection: Reference Collection Close shelf browser (Hides shelf browser)
|
|
|
|
|
|
|
||
| 006.31 MUR Machine learning | 006.31 RAT Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data | 006.312 TOR Data mining with R | 006.312 WIT Data mining | 006.312 YEN Data mining : theories, algorithms, and examples | 006.32 KOS Neural networks and fuzzy systems | 006.32 RAO C++ neural network and fuzzy logic |
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
There are no comments on this title.