Nformation Theory for Data Science: From Entropy to Machine Learning, AI, and Modern Analytics

Name: Nformation Theory for Data Science: From Entropy to Machine Learning, AI, and Modern Analytics
Brand: Independently Published
SKU: 9798199987813
Price: 3765.0 INR
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by Mir Hossain

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ISBN13: 9798199987813
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: June 4, 2026
Pages: 230
Original Price: GBP 33.52
Language: English
Edition: N/A
Item Weight: 404 grams
BISAC Subject(s): Probability & Statistics / General

About The Book

Master the mathematics that powers modern machine learning, artificial intelligence, data analytics, and large language models.

Information theory is the hidden language of data science. Every time a model minimizes cross-entropy loss, every time features are selected using mutual information, and every time an AI system predicts the next token, information theory is at work.

Information Theory for Data Science provides a practical, modern introduction to the concepts that drive today's data-driven technologies. Starting with the foundations of probability and information, this book builds step-by-step toward entropy, divergence measures, feature selection, machine learning applications, deep learning, generative AI, and large language models.

Unlike traditional information theory texts that focus primarily on communication systems, this book emphasizes real-world applications in data science and artificial intelligence, helping readers connect mathematical concepts directly to modern analytics and machine learning workflows.

Inside You'll Learn:

Self-information and surprisal

Shannon entropy and uncertainty measurement

Joint, conditional, and differential entropy

KL divergence and Jensen-Shannon divergence

Mutual information and dependency analysis

Feature selection using information-theoretic methods

Decision trees and entropy-based learning

Cross-entropy loss in machine learning

Information bottleneck theory

Representation learning and latent information

Information theory in deep learning

Natural language processing and language modeling

Computer vision and image information analysis

Generative AI and probabilistic modeling

Data compression and source coding

Channel capacity and reliable communication

R�nyi entropy, Tsallis entropy, and information geometry

Causal information theory

Information theory for Large Language Models (LLMs)

Practical Features

Clear explanations with intuitive examples
Mathematical derivations presented step-by-step
Python implementations throughout the book
Real-world machine learning case studies
Visual diagrams and illustrations
End-of-chapter exercises
Five complete data science projects
Comprehensive formula reference
Interview questions and solutions manual

Whether you are a data scientist, machine learning engineer, AI practitioner, computer science student, researcher, or quantitative analyst, this book will help you develop a deep understanding of how information flows through modern intelligent systems-and how to use that knowledge to build better models and make better decisions.

From entropy to machine learning, AI, and modern analytics, discover the mathematical foundation behind the information age.

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