Data Science for IoT Engineers (eBook)

This book introduces data science to professionals in engineering, physics, mathematics, and related fields. It serves as a workbook with MATLAB code, linking subject knowledge to data science, machine learning, and analytics, with applications in IoT. Part One integrates machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two develops a nonlinear, time-varying machine learning solution for modeling real-life business problems.
Understanding data science is crucial for modern applications, particularly in IoT. This book presents a dynamic machine learning solution to handle these complexities. Topics include machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.
The journey begins with data science and machine learning, covering systems theory and linear algebra. Advanced concepts like the Kalman filter and Bayesian estimation lead to developing a dynamic machine learning model. The book ends with practical applications using digital twins.

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A comprehensive guide for IoT engineers, this book integrates data science, machine learning, and systems analytics to provide a robust understanding of modern techniques.Key FeaturesComprehensive integration of systems theory and machine learningFocus on practical applications like digital twinsLogical progression from basics to advanced techniquesBook DescriptionThis book introduces data science to professionals in engineering, physics, mathematics, and related fields. It serves as a workbook with MATLAB code, linking subject knowledge to data science, machine learning, and analytics, with applications in IoT. Part One integrates machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two develops a nonlinear, time-varying machine learning solution for modeling real-life business problems. Understanding data science is crucial for modern applications, particularly in IoT. This book presents a dynamic machine learning solution to handle these complexities. Topics include machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins. The journey begins with data science and machine learning, covering systems theory and linear algebra. Advanced concepts like the Kalman filter and Bayesian estimation lead to developing a dynamic machine learning model. The book ends with practical applications using digital twins.What you will learnUnderstand data science fundamentalsApply machine learning techniquesUtilize systems theory and linear algebraPerform digital signal processing in machine learningDevelop adaptive machine learning modelsImplement digital twins for causal analysisWho this book is forIdeal for IoT engineers and data scientists, this book requires a basic understanding of mathematics and programming. It is designed for professionals looking to deepen their knowledge in systems theory, machine learning, and analytics.]]>