Khan, Arshad

Business Intelligence and Data Analysis in the Age of AI

Campesato, Oswald

Beginning Python 3 with Claude 3

A Prompt-based Exploration of LLMs

Large Language Models for Developers

Fortino, Andres

Case Studies in Excel, R, and ChatGPT

Data Analysis for Business Decisions

Zherlitsyn, Dmytro

Financial Data Analysis Using Python

Beginning Python 3 with Grok 2

Herman, Erik

Using Analytics and Case Studies

Data Science for Decision Makers

Goralski, Walter

Understanding AI, IoT, 6G and The Infrastructure Revolution

Erik, Herman L.

Theory, Applications and Ethics

AI Revealed

CAMPESATO, OSWALD

CSS3 and SVG with Meta AI

A Systems Analytics Approach

Data Science for IoT Engineers

Contents

Frontmatter

Preface

About the Author

CHAPTER 1 Overview of Data Science

CHAPTER 2 Introduction to Machine Learning

CHAPTER 3 Systems Theory, Linear Algebra, and Analytics Basics

CHAPTER 4 “Modern” Machine Learning

CHAPTER 5 Systems Theory Foundations of Machine Learning

CHAPTER 6 State Space Model and Bayes Filter

CHAPTER 7 The Kalman Filter for Adaptive Machine Learning

CHAPTER 8 The Need for Dynamical Machine Learning: The Bayesian Exact Recursive Estimation

CHAPTER 9 Digital Twins

Epilogue A New Random Field Theory

Index

Madhavan, P. G.

This book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code that creates a common framework and points out various interconnections related to industry. This will allow the reader to connect previous subject knowledge to data science, machine learning, or analytics and apply it to IoT applications. Part One brings together subjects in machine learning, systems theory, linear algebra, digital signal processing, and probability theory.

This book introduces the concepts of data science to professionals in engineering, physics, mathematics, and allied fields. It is a workbook with MATLAB code...

Contents - Contents

Format

Contents

P. G. Madhavan

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