Machine Learning and Big Data

Machine Learning and Big Data

by Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad
Released September 2020
Publisher(s): Wiley-Scrivener
ISBN: 9781119654742

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Book description

Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online survey sites and various news sites, and so on.  To date, various use-cases for this application area are being researched and developed. Software applications are already being published and used in various settings from education and training to discover useful hidden patterns and other information like customer choices and market trends that can help organizations make more informed and customer-oriented business decisions.

Combining BD with ML will provide powerful, largely unexplored application areas that will revolutionize practice in Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Fraud Detection, and so on.  It is very important to continuously monitor and understand these effects from safety and societal point of view.

Hence, the main purpose of this book is for researchers, software developers and practitioners, academicians and students to showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Preface
  5. Section 1: THEORETICAL FUNDAMENTALS
    1. 1 Mathematical Foundation
      1. 1.1 Concept of Linear Algebra
      2. 1.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix
      3. 1.3 Introduction to Calculus
      4. References
    2. 2 Theory of Probability
      1. 2.1 Introduction
      2. 2.2 Independence in Probability
      3. 2.3 Conditional Probability
      4. 2.4 Cumulative Distribution Function
      5. 2.5 Baye’s Theorem
      6. 2.6 Multivariate Gaussian Function
      7. References
    3. 3 Correlation and Regression
      1. 3.1 Introduction
      2. 3.2 Correlation
      3. 3.3 Regression
      4. 3.4 Conclusion
      5. References
  6. Section 2: BIG DATA AND PATTERN RECOGNITION
    1. 4 Data Preprocess
      1. 4.1 Introduction
      2. 4.2 Data Cleaning
      3. 4.3 Data Integration
      4. 4.4 Data Transformation
      5. 4.5 Data Reduction
      6. 4.6 Conclusion
      7. Acknowledgements
      8. References
    2. 5 Big Data
      1. 5.1 Introduction
      2. 5.2 Big Data Evaluation With Its Tools
      3. 5.3 Architecture of Big Data
      4. 5.4 Issues and Challenges
      5. 5.5 Big Data Analytics Tools
      6. 5.6 Big Data Use Cases
      7. 5.7 Where IoT Meets Big Data
      8. 5.8 Role of Machine Learning For Big Data and IoT
      9. 5.9 Conclusion
      10. References
    3. 6 Pattern Recognition Concepts
      1. 6.1 Classifier
      2. 6.2 Feature Processing
      3. 6.3 Clustering
      4. 6.4 Conclusion
      5. References
  7. Section 3: MACHINE LEARNING: ALGORITHMS & APPLICATIONS
    1. 7 Machine Learning
      1. 7.1 History and Purpose of Machine Learning
      2. 7.2 Concept of Well-Defined Learning Problem
      3. 7.3 General-to-Specific Ordering Over Hypotheses
      4. 7.4 Version Spaces and Candidate Elimination Algorithm
      5. 7.5 Concepts of Machine Learning Algorithm
      6. Conclusion
      7. References
    2. 8 Performance of Supervised Learning Algorithms on Multi-Variate Datasets
      1. 8.1 Introduction
      2. 8.2 Supervised Learning Algorithms
      3. 8.3 Classification
      4. 8.4 Neural Network
      5. 8.5 Comparisons and Discussions
      6. 8.6 Summary and Conclusion
      7. References
    3. 9 Unsupervised Learning
      1. 9.1 Introduction
      2. 9.2 Related Work
      3. 9.3 Unsupervised Learning Algorithms
      4. 9.4 Classification of Unsupervised Learning Algorithms
      5. 9.5 Unsupervised Learning Algorithms in ML
      6. 9.6 Summary and Conclusions
      7. References
    4. 10 Semi-Supervised Learning
      1. 10.1 Introduction
      2. 10.2 Training Models
      3. 10.3 Generative Models—Introduction
      4. 10.4 S3VMs
      5. 10.5 Graph-Based Algorithms
      6. 10.6 Multiview Learning
      7. 10.7 Conclusion
      8. References
    5. 11 Reinforcement Learning
      1. 11.1 Introduction: Reinforcement Learning
      2. 11.2 Model-Free RL
      3. 11.3 Model-Based RL
      4. 11.4 Conclusion
      5. References
    6. 12 Application of Big Data and Machine Learning
      1. 12.1 Introduction
      2. 12.2 Motivation
      3. 12.3 Related Work
      4. 12.4 Application of Big Data and ML
      5. 12.5 Issues and Challenges
      6. 12.6 Conclusion
      7. References
  8. Section 4: MACHINE LEARNING’S NEXT FRONTIER
    1. 13 Transfer Learning
      1. 13.1 Introduction
      2. 13.2 Traditional Learning vs. Transfer Learning
      3. 13.3 Key Takeaways: Functionality
      4. 13.4 Transfer Learning Methodologies
      5. 13.5 Inductive Transfer Learning
      6. 13.6 Unsupervised Transfer Learning
      7. 13.7 Transductive Transfer Learning
      8. 13.8 Categories in Transfer Learning
      9. 13.9 Instance Transfer
      10. 13.10 Feature Representation Transfer
      11. 13.11 Parameter Transfer
      12. 13.12 Relational Knowledge Transfer
      13. 13.13 Relationship With Deep Learning
      14. 13.14 Applications: Allied Classical Problems
      15. 13.15 Further Advancements and Conclusion
      16. References
  9. Section 5: HANDS-ON AND CASE STUDY
    1. 14 Hands on MAHOUT—Machine Learning Tool
      1. 14.1 Introduction to Mahout
      2. 14.2 Installation Steps of Apache Mahout Using Cloudera
      3. 14.3 Installation Steps of Apache Mahout Using Windows 10
      4. 14.4 Installation Steps of Apache Mahout Using Eclipse
      5. 14.5 Mahout Algorithms
      6. 14.6 Conclusion
      7. References
    2. 15 Hands-On H2O Machine Learning Tool
      1. 15.1 Introduction
      2. 15.2 Installation
      3. 15.3 Interfaces
      4. 15.4 Programming Fundamentals
      5. 15.5 Machine Learning in H2O
      6. 15.6 Applications of H2O
      7. 15.7 Conclusion
      8. References
    3. 16 Case Study: Intrusion Detection System Using Machine Learning
      1. 16.1 Introduction
      2. 16.2 System Design
      3. 16.3 Existing Proposals
      4. 16.4 Approaches Used in Designing the Scenario
      5. 16.5 Result Analysis
      6. 16.6 Conclusion
      7. References
    4. 17 Inclusion of Security Features for Implications of Electronic Governance Activities
      1. 17.1 Introduction
      2. 17.2 Objective of E-Governance
      3. 17.3 Role of Identity in E-Governance
      4. 17.4 Status of E-Governance in Other Countries
      5. 17.5 Pros and Cons of E-Governance
      6. 17.6 Challenges of E-Governance in Machine Learning
      7. 17.7 Conclusion
      8. References
  10. Index
  11. End User License Agreement

Product information

  • Title: Machine Learning and Big Data
  • Author(s): Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad
  • Release date: September 2020
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119654742