“…Big data, artificial intelliegence and data analysis set…”

“…Big data, artificial intelligence and data analysis set…”

Materias:

“…Innovation, entrepreneurship and management series. Big data, artificial intelligence, and data analysis set…”

“…Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set…”

Materias:

Materias:

Materias:

Materias:

Materias:

Materias:

Materias:

Materias:

Tabla de Contenidos: “…Cover; Title Page; Copyright; Preface; Contents; Figures; Tables; Summary; Acknowledgments; Abbreviations; Chapter One: Big Data: Challenges and Opportunities; What Is "Big Data"?…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Exploring Machine Learning -- Exploring Supervised Methods -- Exploring Nonlinear Models -- Exploring Ensemble Methods -- Exploring Unsupervised Methods -- Exploring Cluster Methods -- Exploring Dimension Reduction -- Exploring Deep Learning -- Conclusion -- Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks -- Big Data -- Big Data Features -- Impact of Big Data on Business and People -- Better Customer Relationships -- Refined Product Development -- Improved Decision-Making -- Big Data Warehousing -- Big Data ETL -- Big Data Frameworks -- Apache Spark -- Resilient Distributed Data Sets -- Spark Configuration -- Spark Frameworks -- SparkSQL -- Spark Streaming -- Spark MLlib -- GraphX -- ML Frameworks -- Scikit-Learn -- H2O -- XGBoost -- DL Frameworks -- Keras -- Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O -- Exploring the Ordinary Least-Squares Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 4: Survival Analysis with PySpark and Lifelines -- Exploring Survival Analysis -- Exploring Cox Proportional Hazards Method -- Lifeline in Action -- Exploring the Accelerated Failure Time Method -- PySpark in Action -- Conclusion -- Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O -- Exploring the Logistic Regression Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O -- Decision Trees -- Preprocessing Features -- Scikit-Learn in Action -- Gradient Boosting -- XGBoost in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O…”

Tabla de Contenidos: “…Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Big Data and Hadoop; V's of big data; Volume; Velocity; Variety; Understanding big data; NoSQL; Types of NoSQL databases; Analytical database; Who is creating the big data?…”

“…Plan and Implement Hadoop Virtualization for Maximum Performance, Scalability, and Business Agility Enterprises running Hadoop must absorb rapid changes in big data ecosystems, frameworks, products, and workloads. …”

“…Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. …”

Tabla de Contenidos: “…Preface -- Acknowledgement -- 1 A Systematic Literature Review on Cyber Security Threats of Industrial Internet of Things 1 Ravi Gedam and Surendra Rahamatkar -- 1.1 Introduction -- 1.2 Background of Industrial Internet of Things -- 1.3 Literature Review -- 1.4 The Proposed Methodology -- 1.5 Experimental Requirements -- 1.6 Conclusion -- 2 Integration of Big Data Analytics Into Cyber-Physical Systems 19 Nandhini R.S. and Ramanathan -- 2.1 Introduction -- 2.2 Big Data Model for Cyber-Physical System -- 2.3 Big Data and Cyber-Physical System Integration -- 2.4 Storage and Communication of Big Data for Cyber-Physical System -- 2.5 Big Data Processing in Cyber-Physical System -- 2.6 Applications of Big Data for Cyber-Physical System -- 2.7 Security and Privacy -- 2.8 Conclusion -- 3 Machine Learning: A Key Towards Smart Cyber-Physical Systems 43 Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur -- 3.1 Introduction -- 3.2 Different Machine Learning Algorithms -- 3.3 ML Use-Case in MATLAB -- 3.4 ML Use-Case in Python -- 3.5 Conclusion -- 4 Precise Risk Assessment and Management 63 Ambika N. -- 4.1 Introduction -- 4.2 Need for Security -- 4.3 Different Kinds of Attacks -- 4.4 Literature Survey -- 4.5 Proposed Work -- 4.6 Conclusion -- 5 A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment 85 Rajarajan Ganesarathinam, Muthukumaran Singaraveluand K.N. …”

“…Non-official sources of data, big data in particular, are currently attracting enormous interest in the world of official statistics. …”