Big Data Analytics Methods Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing

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...

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Bibliographic Details
Main Author: Ghavami, Peter
Format: eBook
Language:English
Published: Berlin ; Boston De Gruyter, [2019]
Edition:2nd Edition.
Subjects:
Online Access:EBSCOhost
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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] 
300 |a 1 online resource (XVI, 238 pages) 
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{u2014}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{u2014}Artificial Neural Networks --  |t Chapter 9. Model Accuracy and Optimization --  |t Part III: Case Study{u2014}Prediction and Advanced Analytics in Practice --  |t Chapter 10. Ensemble of Models{u2014}Medical Prediction Case Study: Data Types, Data Requirements and Data Pre-Processing --  |t Appendices --  |t References --  |t Index 
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. 
546 |a In English. 
555 0 |a Available in electronic full text to members of the University via the Library web catalogue. 
588 0 |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 21. Dez 2019). 
653 4 |a Big data. 
653 4 |a Data analysis. 
653 4 |a Data mining. 
653 4 |a Machine learning. 
653 4 |a Neural networks. 
653 7 |a BUSINESS & ECONOMICS / Information Management.  |2 bisacsh 
653 0 |a Big data. 
653 0 |a Data mining. 
653 0 |a Natural language processing (Computer science) 
653 7 |a Big data.  |2 fast  |0 (OCoLC)fst01892965 
653 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
653 7 |a Natural language processing (Computer science)  |2 fast  |0 (OCoLC)fst01034365 
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