<電子ブック>
Data Analytics and Machine Learning : Navigating the Big Data Landscape / edited by Pushpa Singh, Asha Rani Mishra, Payal Garg
(Studies in Big Data. ISSN:21976511 ; 145)
版 | 1st ed. 2024. |
---|---|
出版者 | Singapore : Springer Nature Singapore : Imprint: Springer |
出版年 | 2024 |
本文言語 | 英語 |
大きさ | XIII, 353 p. 157 illus., 125 illus. in color : online resource |
著者標目 | Singh, Pushpa editor Mishra, Asha Rani editor Garg, Payal editor SpringerLink (Online service) |
件 名 | LCSH:Quantitative research LCSH:Machine learning LCSH:Natural language processing (Computer science) FREE:Data Analysis and Big Data FREE:Machine Learning FREE:Natural Language Processing (NLP) |
一般注記 | Chapter 1. Introduction to Data Analytics, Big Data, and Machine Learning -- Chapter 2. Fundamentals of Data Analytics and Lifecycle -- Chapter 3. Building Predictive Models with Machine Learning -- Chapter 4. Stream data model and architecture -- Chapter 5. Leveraging Big Data for Data Analytics -- Chapter 6. Advanced Techniques in Data Analytics -- Chapter 7. Scalable Machine Learning with Big Data -- Chapter 8. Big Data Analytics Framework using Machine Learning on Massive Datasets -- Chapter 9. Deep-learning Techniques in Big-Data analytics -- Chapter 10. Data Privacy and Ethics in Data Analytics -- Chapter 11. Practical Implementation of Machine Learning Techniques & data analytics using R -- Chapter 12. Real-World Applications of Data Analytics, Big Data, and Machine Learning -- Chapter 13. Implementing Data-Driven Innovation in Organizations -- Chapter 14. Business Transformation using Big Data Analytics and Machine Learning -- Chapter 15. Future Trends and Emerging Opportunities in HealthAnalytics -- Chapter 16. Future Trends in Data Analytics and Machine Learning This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data HTTP:URL=https://doi.org/10.1007/978-981-97-0448-4 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
|
Springer eBooks | 9789819704484 |
|
電子リソース |
|
EB00235642 |
書誌詳細を非表示
データ種別 | 電子ブック |
---|---|
分 類 | LCC:QA76.9.Q36 DC23:001.422 DC23:005.7 |
書誌ID | 4001108603 |
ISBN | 9789819704484 |