Big Data Analytics Methods : : Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing / / Peter Ghavami.
Big 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. Mo...
Saved in:
| Superior document: | Title is part of eBook package: De Gruyter DG Ebook Package English 2020 |
|---|---|
| VerfasserIn: | |
| Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2019] ©2020 |
| Year of Publication: | 2019 |
| Edition: | 2nd Edition |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (XVI, 238 p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 05663nam a22009255i 4500 | ||
|---|---|---|---|
| 001 | 9781547401567 | ||
| 003 | DE-B1597 | ||
| 005 | 20230127011820.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr || |||||||| | ||
| 008 | 230127t20192020gw fo d z eng d | ||
| 020 | |a 9781547401567 | ||
| 024 | 7 | |a 10.1515/9781547401567 |2 doi | |
| 035 | |a (DE-B1597)522386 | ||
| 035 | |a (OCoLC)1135579330 | ||
| 040 | |a DE-B1597 |b eng |c DE-B1597 |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c DE | ||
| 072 | 7 | |a BUS083000 |2 bisacsh | |
| 084 | |a ST 530 |2 rvk |0 (DE-625)rvk/143679: | ||
| 100 | 1 | |a Ghavami, Peter, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Big Data Analytics Methods : |b Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing / |c Peter Ghavami. |
| 250 | |a 2nd Edition | ||
| 264 | 1 | |a Berlin ; |a Boston : |b De Gruyter, |c [2019] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource (XVI, 238 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | 0 | |t Frontmatter -- |t Acknowledgments -- |t About the Author -- |t Contents -- |t Introduction -- |t Part I: Big Data Analytics -- |t Chapter 1. Data Analytics Overview -- |t Chapter 2. Basic Data Analysis -- |t Chapter 3. Data Analytics Process -- |t Part II: Advanced Analytics Methods -- |t Chapter 4. Natural Language Processing -- |t Chapter 5. Quantitative Analysis—Prediction and Prognostics -- |t Chapter 6. Advanced Analytics and Predictive Modeling -- |t Chapter 7. Ensemble of Models: Data Analytics Prediction Framework -- |t Chapter 8. Machine Learning, Deep Learning—Artificial Neural Networks -- |t Chapter 9. Model Accuracy and Optimization -- |t Part III: Case Study—Prediction and Advanced Analytics in Practice -- |t Chapter 10. Ensemble of Models—Medical Prediction Case Study: Data Types, Data Requirements and Data Pre-Processing -- |t Appendices -- |t References -- |t Index |
| 506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
| 520 | |a Big 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 | |a Issued also in print. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 27. Jan 2023) | |
| 650 | 7 | |a BUSINESS & ECONOMICS / Information Management. |2 bisacsh | |
| 653 | |a Big data. | ||
| 653 | |a Data analysis. | ||
| 653 | |a Data mining. | ||
| 653 | |a Machine learning. | ||
| 653 | |a Neural networks. | ||
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t DG Ebook Package English 2020 |z 9783110696288 |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t DG Plus DeG Package 2020 Part 1 |z 9783110696271 |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2019 English |z 9783110610765 |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2019 |z 9783110664232 |o ZDB-23-DGG |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Engineering, Computer Sciences 2019 English |z 9783110610154 |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Engineering, Computer Sciences 2019 |z 9783110606096 |o ZDB-23-DEI |
| 776 | 0 | |c EPUB |z 9781547401581 | |
| 776 | 0 | |c print |z 9781547417957 | |
| 856 | 4 | 0 | |u https://doi.org/10.1515/9781547401567 |
| 856 | 4 | 0 | |u https://www.degruyter.com/isbn/9781547401567 |
| 856 | 4 | 2 | |3 Cover |u https://www.degruyter.com/document/cover/isbn/9781547401567/original |
| 912 | |a 978-3-11-061015-4 EBOOK PACKAGE Engineering, Computer Sciences 2019 English |b 2019 | ||
| 912 | |a 978-3-11-061076-5 EBOOK PACKAGE COMPLETE 2019 English |b 2019 | ||
| 912 | |a 978-3-11-069627-1 DG Plus DeG Package 2020 Part 1 |b 2020 | ||
| 912 | |a 978-3-11-069628-8 DG Ebook Package English 2020 |b 2020 | ||
| 912 | |a EBA_BACKALL | ||
| 912 | |a EBA_CL_CHCOMSGSEN | ||
| 912 | |a EBA_CL_LAEC | ||
| 912 | |a EBA_DGALL | ||
| 912 | |a EBA_EBACKALL | ||
| 912 | |a EBA_EBKALL | ||
| 912 | |a EBA_ECL_CHCOMSGSEN | ||
| 912 | |a EBA_ECL_LAEC | ||
| 912 | |a EBA_EEBKALL | ||
| 912 | |a EBA_ESSHALL | ||
| 912 | |a EBA_ESTMALL | ||
| 912 | |a EBA_SSHALL | ||
| 912 | |a EBA_STMALL | ||
| 912 | |a GBV-deGruyter-alles | ||
| 912 | |a PDA11SSHE | ||
| 912 | |a PDA12STME | ||
| 912 | |a PDA13ENGE | ||
| 912 | |a PDA17SSHEE | ||
| 912 | |a PDA18STMEE | ||
| 912 | |a PDA5EBK | ||
| 912 | |a ZDB-23-DEI |b 2019 | ||
| 912 | |a ZDB-23-DGG |b 2019 | ||