Introduction to Machine Learning with Security: Theory and Practice Using Python in the Cloud (Synthesis Lectures on Engineering, Science, and Technology) - Tapa dura

Acerca del autor

Pramod Gupta has more than 20 years of experience as a researcher and academician in various organizations including work with NASA, GE, VISA, and University of California and startups. He has a PhD from McMaster University in Electrical and Computer Engineering with specialization in Neuro-Control of Robotic Manipulators.  He has more than 40 publications on these subjects. His research areas include Neural Networks, Machine Learning, Artificial Intelligence, Data Modeling and Analytics and Data mining.  Presently, he is an Adjunct Faculty and working as an independent data science consultant. 

Naresh K. Sehgal is a CTO at the Deeply Human AI. Before that he worked at NovaSignal for 3 years and at Intel for 31 years in various Engineering and Management roles. Naresh has earned his B.E. from Punjab Engineering College, M.S. and Ph.D. from Syracuse University. He also taught a Cloud Computing class at Santa Clara University, where he earned an MBA.

John M. Acken is a research faculty member in the Electrical and Computer Engineering Department, Portland State University, Portland, OR.  John received his BS and MS in Electrical Engineering from Oklahoma State University and Ph. D. in Electrical Engineering from Stanford University. His projects include technology and devices for information security and identity authentication. John has worked as an Electrical Engineer and Manager at several companies, including the US Army, Sandia National Labs in Albuquerque, New Mexico and Intel in Santa Clara, CA.  John’s time in the US Army was in the Army Security Agency, a branch of NSA during the Vietnam War.

De la contraportada

This book provides an introduction to machine learning and cloud computing, both from conceptual and practical levels, along with their usage with a Public Cloud infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and security considerations, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and several case studies.

In addition, this book:

• Provides broad coverage of AI, Machine Learning and Cloud Computing;
• Uses real examples and case studies to demonstrate key topics;
• Demonstrates concepts, as well as practical usage.