Data Science for IoT Engineers (eBook, ePUB)
Master Data Science Techniques and Machine Learning Applications for Innovative IoT Solutions
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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 ma...
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.
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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