Suman Rajest S, Regin R, Bhopendra Singh, Arlin Rooshma, Ahmed J. Obaid (Editors)
Describes the advances of machine learning, guiding you through setting up with ICT frameworks.
Represents ICT based Framework for Data Science and Machine Learning real-life case studies and examples
Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis and Machine Learning Applications
The book begins with a basic overview of machine learning, guiding you through setting up with ICT frameworks. The popular neural network architectures such as CNNs and RNNs, with the help of data and learning how to build models, have been covered. This book provides a strong foundation in machine learning using ICT by providing real-life case studies and examples. The advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered.
The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and a step-by-step approach to exploring, building, evaluating, and optimizing machine learning models. This book is also a rigorous and informative work presenting the mathematics behind modern machine learning techniques. The book is aimed at a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist, or practitioner, who is interested in fast simulation, including rare-event probability estimation, efficient combinatorial and continuous multi-extremal optimization, and machine learning algorithms.
