Machine Learning and Big dаta: Concepts, Algorithms, Tools and Applications

Machine Learning and Big dаta: Concepts, Algorithms, Tools and Applications
PDF, ePUB
  • eBook:
    Machine Learning and Big dаta: Concepts, Algorithms, Tools and Applications
  • Author:
    Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 1, 2020
  • ISBN:
    1119654742
  • ISBN-13:
    9781119654742
  • Language:
    English
  • Pages:
    544 pages
  • Format:
    PDF, ePUB

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

Content

Section 1: Theoretical Fundamentals
1. Mathematical Foundation
2. Theory of Probability
3. Correlation and Regression

Section 2: Big Data and Pattern Recognition
4. Data Preprocess
5. Big Data
6. Pattern Recognition Concepts

Section 3: Machine Learning: Algorithms & Applications
7. Machine Learning
8. Performance of Supervised Learning Algorithms on Multi-Variate Datasets
9. Unsupervised Learning
10. Semi-Supervised Learning
11. Reinforcement Learning
12. Application of Big Data and Machine Learning

Section 4: Machine Learning’s Next Frontier
13. Transfer Learning

Section 5: Hands-On and Case Study
14. Hands on MAHOUT—Machine Learning Tool
15. Hands-On H2O Machine Learning Tool
16. Case Study: Intrusion Detection System Using Machine Learning
17. Inclusion of Security Features for Implications of Electronic Governance Activities

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