Data Science for IoT Engineers (eBook)
170 Seiten
Packt Publishing (Verlag)
978-1-83664-188-9 (ISBN)
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.
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.]]>
Erscheint lt. Verlag | 30.7.2024 |
---|---|
Sprache | englisch |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Mathematik / Informatik ► Informatik ► Theorie / Studium | |
ISBN-10 | 1-83664-188-5 / 1836641885 |
ISBN-13 | 978-1-83664-188-9 / 9781836641889 |
Haben Sie eine Frage zum Produkt? |
![EPUB](/img/icon_epub_big.jpg)
Digital Rights Management: ohne DRM
Dieses eBook enthält kein DRM oder Kopierschutz. Eine Weitergabe an Dritte ist jedoch rechtlich nicht zulässig, weil Sie beim Kauf nur die Rechte an der persönlichen Nutzung erwerben.
Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür die kostenlose Software Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür eine kostenlose App.
Geräteliste und zusätzliche Hinweise
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich