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srivastava p methodologies frameworks applications machine learning 2024

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Textbook in PDF format In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of the latest advancements, theoretical foundations, and practical applications in the realm of Machine Learning. Technology is constantly evolving, and Machine Learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest Machine Learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine Learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. Academic scholars seeking to unlock the potential of Machine Learning in the context of Industry 5.0 and advanced IoT applications will find that the groundbreaking book, Methodologies, Frameworks, and Applications of Machine Learning, introduces an unmissable opportunity to delve into the forefront of modern research and application. This book offers a wealth of knowledge and practical insights across a wide array of topics, ranging from conceptual frameworks and methodological approaches to the application of probability theory, statistical techniques, and Machine Learning in domains as diverse as e-government, healthcare, cyber-physical systems, and sustainable development, this comprehensive guide equips you with the tools to navigate the complexities of Industry 5.0 and the Internet of Things (IoT). Machine Learning stands as a pool of critical tools that has the potential to enable the emergence of Industry 5.0 through advanced IoT applications. The objective is to develop models capable of learning autonomously from data, context, and environment. The continuous development of novel algorithms, coupled with the growing availability of realistic data and low-cost computation, has led to significant strides in Machine Learning, with data-intensive methodologies finding application in various industries. This book takes a conceptual and practical approach to Machine Learning algorithms. It enables students, academics, researchers, engineers, and practitioners to navigate the challenges of Industry 5.0 implementation by utilizing Machine Learning algorithms and deploying advanced IoT applications that address social, economic, political, privacy, and security concerns. The Chapter 1 provides an in-depth exploration of Reinforcement Learning (RL), a subfield of Machine Learning that empowers agents to interact with their environment and learn from experiences. Core elements of RL, including agents, actions, states, and rewards, are dissected alongside algorithms like policy gradients, SARSA, and Q-learning. Challenges such as the exploration-exploitation conflict and Deep Learning instability are discussed. The chapter concludes with a comprehensive list of RL applications in diverse industries, from robotics and gaming to banking and healthcare, highlighting the need for further research to fully realize RL's transformative potential. The Chapter 2 focuses on practical implementations of Machine Learning projects using Scikit-learn and TensorFlow libraries in Python. Four distinct projects unfold, each addressing classification, regression, and image classification problems. The step-by-step walkthrough covers model evaluation using classical Machine Learning techniques and deep neural networks. The projects delve into real-world datasets, such as White Wine Quality and stock market closing values, providing readers with hands-on experience in applying Machine Learning frameworks to diverse problem domains

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srivastava p methodologies frameworks applications machine learning 2024

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