Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications - Hardcover

Über die Autorin bzw. den Autor

Uma N. Dulhare is a Professor in the Department of Computer Science & Eng., MJCET affiliated to Osmania University, Hyderabad, India. She has more than 20 years teaching experience years with many publications in reputed international conferences, journals and online book chapter contributions. She received her PhD from Osmania University, Hyderabad.

Khaleel Ahmad is an Assistant Professor in the Department of Computer Science & Information Technology at Maulana Azad National Urdu University, Hyderabad, India. He holds a PhD in Computer Science & Engineering. He has published more than 25 papers in refereed journals and conferences as well as edited two books.

Khairol Amali bin Ahmad obtained a BSc in Electrical Engineering in 1992 from the United States Military Academy, West Point, MSc in Military Electronic Systems Engineering in 1999 from Cranfield University, England, and PhD from ISAE-SUPAERO, France in 2015. Currently, he is the Dean of the Engineering Faculty at the National Defense University of Malaysia.

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This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations.

The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.

Subjects covered in detail include:

• Mathematical foundations of machine learning with various examples.
• An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.
• Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.
• Hands-on machine leaning open source tools viz. Apache Mahout, H₂O.
• Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.
• Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Audience
Researchers and engineers in artificial intelligence, information technologies, computer science as well as software developers and product/process managers.