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| 001 | 9781547401567 | ||
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| 100 | 1 |
_aGhavami, Peter, _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _975635 |
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| 245 | 1 | 0 |
_aBig Data Analytics Methods : _bAnalytics Techniques in Data Mining, Deep Learning and Natural Language Processing / _cPeter Ghavami. |
| 250 | _a2nd Edition | ||
| 264 | 1 |
_aBerlin ; _aBoston : _bDe Gruyter, _c[2019] |
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| 264 | 4 | _c©2020 | |
| 300 | _a1 online resource (XVI, 238 p.) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | 0 |
_tFrontmatter -- _tAcknowledgments -- _tAbout the Author -- _tContents -- _tIntroduction -- _tPart I: Big Data Analytics -- _tChapter 1. Data Analytics Overview -- _tChapter 2. Basic Data Analysis -- _tChapter 3. Data Analytics Process -- _tPart II: Advanced Analytics Methods -- _tChapter 4. Natural Language Processing -- _tChapter 5. Quantitative Analysis-Prediction and Prognostics -- _tChapter 6. Advanced Analytics and Predictive Modeling -- _tChapter 7. Ensemble of Models: Data Analytics Prediction Framework -- _tChapter 8. Machine Learning, Deep Learning-Artificial Neural Networks -- _tChapter 9. Model Accuracy and Optimization -- _tPart III: Case Study-Prediction and Advanced Analytics in Practice -- _tChapter 10. Ensemble of Models-Medical Prediction Case Study: Data Types, Data Requirements and Data Pre-Processing -- _tAppendices -- _tReferences -- _tIndex |
| 506 | 0 |
_arestricted access _uhttp://purl.org/coar/access_right/c_16ec _fonline access with authorization _2star |
|
| 520 | _aBig Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics. | ||
| 530 | _aIssued also in print. | ||
| 538 | _aMode of access: Internet via World Wide Web. | ||
| 546 | _aIn English. | ||
| 588 | 0 | _aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 27. Jan 2023) | |
| 650 | 7 |
_aBUSINESS & ECONOMICS / Information Management. _2bisacsh _975636 |
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| 653 | _aBig data. | ||
| 653 | _aData analysis. | ||
| 653 | _aData mining. | ||
| 653 | _aMachine learning. | ||
| 653 | _aNeural networks. | ||
| 773 | 0 | 8 |
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_iTitle is part of eBook package: _dDe Gruyter _tDG Plus DeG Package 2020 Part 1 _z9783110696271 |
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_iTitle is part of eBook package: _dDe Gruyter _tEBOOK PACKAGE Engineering, Computer Sciences 2019 English _z9783110610154 |
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