Introduction to Machine Learning in the Cloud with Python
Description
This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.
Reviews / Votes
"Considering the technical content, the authors' prose and presentation style is conversational and easy to follow. There is a useful and extensive glossary at the book's conclusion, which will also be helpful to readers new to the subject matter. . Overall, the book represents a valuable resource for students and practitioners who need a well-organized and somewhat in-depth overview of how machine learning can be performed using existing cloud resources." (Harry J. Foxwell, Computing Reviews, February 24, 2022)More details
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Persons
Dr. Pramod Gupta is working as a Senior Principal Data Scientist at NovaSignal. 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.
Dr. Naresh K. Sehgal is working as a Senior VP of Cloud Engineering at NovaSignal. Prior to that he spent 31 years at Intel in various technical and management roles. Naresh completed his B.E. from Punjab Engineering College, M.S. and Ph.D. from Syracuse University. Naresh has taught a Cloud Computing class at Santa Clara University, where healso earned a MBA. Naresh is the co-author of two books on Cloud Computing and Security. In addition, he has 5 patents and over 30 published papers.
Content
Introduction to Machine Learning.- Foundations of Machine Learning.- Deep Learning and cloud computing.- Cloud Computing Concepts.- Practical Aspects in Machine Learning.- Artificial Intelligence and Information Security.- Examples of Analytics in the Cloud.- A case study of Data Collection and Analytics in the Cloud.- Future trends in Hardware based AI and ML.