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Data Science for IoT Engineers
Data Science for IoT Engineers

Data Science for IoT Engineers: Master Data Science Techniques and Machine Learning Applications for Innovative IoT Solutions

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Profile Icon Mercury Learning and Information Profile Icon P. G. Madhavan
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£37.99
eBook Jul 2024 170 pages 1st Edition
eBook
£37.99
Arrow left icon
Profile Icon Mercury Learning and Information Profile Icon P. G. Madhavan
Arrow right icon
£37.99
eBook Jul 2024 170 pages 1st Edition
eBook
£37.99
eBook
£37.99

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Key benefits

  • Comprehensive integration of systems theory and machine learning
  • Focus on practical applications like digital twins
  • Logical progression from basics to advanced techniques

Description

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.

Who is this book for?

Ideal 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.

What you will learn

  • Understand data science fundamentals
  • Apply machine learning techniques
  • Utilize systems theory and linear algebra
  • Perform digital signal processing in machine learning
  • Develop adaptive machine learning models
  • Implement digital twins for causal analysis

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 30, 2024
Length: 170 pages
Edition : 1st
Language : English
ISBN-13 : 9781836641889
Category :

What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
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Payment Processing...
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Product Details

Publication date : Jul 30, 2024
Length: 170 pages
Edition : 1st
Language : English
ISBN-13 : 9781836641889
Category :

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Table of Contents

4 Chapters
About the Authors Chevron down icon Chevron up icon
Part 1     Machine Learning from Multiple Perspectives Chevron down icon Chevron up icon
Part 2     Systems Analytics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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