Books

What really happens inside your computer?A click seems simple.A program opens.A file loads.A webpage appears.But underneath that simple experience, billions of electronic components and thousands of layers of software are working together.Inside the Machine takes you beneath the interface—from bits and logic gates to CPUs, memory, storage, operating systems, and applications.Discover how information becomes computation, how processors execute instructions, how memory and caches keep data moving, how operating systems manage the machine, and how a computer goes from power-on to a running application.Stop seeing the computer as a black box. Start seeing the machine.

AI can analyze the data. But who decides what the data means—and what to do next?Modern organizations have more information than ever, yet making good decisions remains difficult.AI-Powered Smart Decision Making shows how to combine AI, data, analytics, and human judgment to turn information into insight and insight into action.Discover a practical framework for making better decisions across strategy, marketing, sales, operations, finance, risk, and beyond.AI is not the decision. AI improves the decision process. Table of ContentsIntroduction—The New Era of AI-Powered Decision MakingChapter 1 — The Foundations of Better Business DecisionsChapter 2 — From Intuition to Data-Driven Decision MakingChapter 3 — Understanding Data for Better DecisionsChapter 4 — Business Analytics: Turning Data into InsightChapter 5 — How AI Changes Business Decision MakingChapter 6 — Building an AI-Powered Decision FrameworkChapter 7 — Asking the Right Questions: AI, Data & Decision ProblemsChapter 8 — Predictive Decision MakingChapter 9 — Prescriptive Analytics and Decision OptimizationChapter 10 — AI for Strategic Business DecisionsChapter 11 — AI for Marketing and Customer DecisionsChapter 12 — AI for Sales, Operations and Supply ChainChapter 13 — AI for Financial and Risk DecisionsChapter 14 — Human + AI: The New Decision-Making PartnershipChapter 15 — Bias, Uncertainty and the Limits of AI DecisionsChapter 16 — Trustworthy, Responsible and Explainable AI DecisionsChapter 17 — Measuring Decision Quality and Business ImpactChapter 18 — Building an AI-Powered Decision CultureChapter 19 — Designing an AI-Powered Decision SystemChapter 20 — The Future of Smart Decision MakingConclusion — From Data to Better DecisionsDisclaimer

Master Operations Research Through Concepts, Techniques, and Practical Problem-SolvingLearn Linear Programming, Simplex Methods, Transportation, Assignment, TSP, Network Analysis, CPM, PERT, Queuing Theory, Game Theory, and Sequencing through structured explanations, numerical examples, and step-by-step problem-solving techniques.

Start Your Computer Science Journey with Strong FundamentalsA beginner-friendly guide covering computer fundamentals, number systems, algorithms, programming, problem-solving, OOP, data structures, and file handling—with clear explanations, practical examples, exercises, and a step-by-step learning approach.

AI is no longer just a technology experiment.It is becoming part of how businesses communicate, process information, serve customers, manage workflows, analyze data, and make decisions.But where should AI actually be used?AI-Driven Operations provides a practical roadmap for answering that question.Discover how to:Find the right processes to automateTurn manual workflows into intelligent workflowsUse AI agents responsiblyConnect AI with business systemsReduce operational costsMeasure real business valueProtect data and maintain human oversightScale successful AI initiativesThe future of business is not about using the most AI.It is about using AI intelligently.

AI can generate an answer. But can it safely take action?AI agents are changing software by giving intelligent systems the ability to plan, use tools, interact with external systems, and execute multi-step workflows.Moving from an impressive prototype to a dependable production system, however, requires much more than a powerful model.AI Agents & Agentic Workflows in Production explores the engineering principles behind production-grade agentic systems—from architecture and tool use to memory, planning, orchestration, evaluation, security, reliability, observability, scalability, governance, and continuous operation.Learn how to build agents that don't just act—but act reliably, safely, measurably, and under control.

Security is no longer something you add after a system is built. It must be designed from the beginning.Security by Design explores how modern digital systems can be built to reduce risk, limit unauthorized access, protect sensitive data, detect threats, contain failures, and recover from attacks.From secure architecture and cybersecurity foundations to privacy, cloud security, DevSecOps, AI security, and digital resilience, this book provides a practical foundation for building systems that remain secure in an increasingly connected and AI-powered world.

The Future of Software Engineering Has Arrived.AI can now write code, generate tests, analyze requirements, explain systems, assist with debugging, and automate increasingly complex engineering tasks.But building great software has never been just about writing code.How do you design the right architecture?How do you build systems that remain secure and reliable at scale?How do you integrate AI without sacrificing quality, privacy, or control?How do you use AI to make engineers more effective without replacing engineering judgment?Engineering Software Systems in the AI Era explores these questions and provides a practical framework for designing, building, testing, securing, deploying, operating, and evolving modern software systems.Inside, you will explore:Modern software architecture and system designCloud-native and scalable systemsData architecture and intelligent data systemsAI engineering and AI-native applicationsCybersecurity and privacy by designSoftware quality and reliabilityDevOps, DevSecOps, and continuous deliveryObservability and production engineeringAI-augmented software developmentAI agents and intelligent engineering workflowsPlatform engineeringTechnical debt and engineering strategyAI governance and risk managementTechnical leadership in the AI eraThe future of software engineeringThe central message is simple:AI may make software development faster. Engineering makes software better.Learn how to combine the power of artificial intelligence with the discipline of software engineering to build systems that are secure, scalable, reliable, intelligent, maintainable, and ready for the future.

Your AI assistant forgets you every time the session ends. Building Persistent AI shows you how to fix that — by moving memory, personality, and identity out of the model and into a file you own and control.

What if you could build software without manually writing every line of code? Artificial Intelligence is transforming the way software is designed, developed, tested, and deployed.But becoming an AI-powered software engineer isn't simply about asking an AI to write code.It's about learning how to think, design, build, test, verify, and improve software with AI as your engineering partner.The AI-Powered Software Engineer takes you on a practical journey from traditional programming to AI-assisted development and modern AI engineering.Learn how to use AI to:Design software. Generate and understand code. Debug complex problems. Build AI-powered applications. Automate workflows. Test and secure software. Deploy real products. Build AI-powered SaaS businesses.You don't need to become the fastest programmer in the room.You need to become the person who can turn an idea into a working, reliable, valuable system.Write less code. Think more. Build faster. Engineer smarter.The future of software engineering isn't humans versus AI.It's humans building with AI.

A practical implementation framework covering 22 essential data center operational domains. Assess gaps, establish priorities and build a controlled improvement roadmap.

Go beyond writing code and learn what it takes to build software that lasts. Fundamentals of Software Engineering walks you through requirements, architecture, testing, deployment and production using practical trade-offs and a real-world case study. Follow TaskFlow from its first requirement to a system built to scale, evolve and survive the real world.

Statistics is the language of uncertainty. AI is the science of learning from data. Together, they provide a powerful foundation for modern intelligent systems.Applied Statistics with AI: Hypothesis Testing and Inference for Modern Models is designed for learners who want to understand how statistical methods can be applied to Artificial Intelligence, Machine Learning, Data Science, and modern research.The book begins with essential statistical foundations, including data types, sampling, preprocessing, descriptive statistics, visualization, probability, and probability distributions. It then builds toward statistical inference, covering estimation, Maximum Likelihood Estimation, Bayesian estimation, hypothesis testing, p-values, confidence intervals, Type I and Type II errors, and statistical significance.Readers will learn how classical statistical tests such as t-tests, ANOVA, and Chi-Square tests can be used in AI-related contexts, along with non-parametric methods such as Wilcoxon, Mann-Whitney, Kruskal-Wallis, and Kolmogorov-Smirnov tests.The book then connects statistics directly to Machine Learning through regression, model evaluation, cross-validation, resampling, feature selection, PCA, regularization, A/B testing, and statistical power.Advanced chapters explore Bayesian inference, MCMC, causal inference, uncertainty quantification in deep learning, and confidence estimation in AI predictions. Real-world case studies demonstrate how statistical inference can support applications in healthcare, finance, Natural Language Processing, and Computer Vision.The book also addresses an increasingly important dimension of AI: responsible statistical practice. Readers will explore bias detection, fairness, ethical hypothesis testing, and the responsible interpretation of AI research results.Finally, the book looks ahead to automated statistical inference, AI-driven hypothesis generation, and emerging research challenges.Whether you are a student learning statistics for AI, a researcher evaluating machine learning experiments, a data scientist analyzing evidence, or an AI practitioner seeking stronger statistical foundations, this book provides a structured path from fundamental concepts to modern applications.Understand the data. Test the hypothesis. Quantify uncertainty. Make better AI decisions.

Learn how to create, design, and integrate game audio and music using Unity and FMOD, from sound effects and ambience to interactive audio systems and dynamic music. Includes a complete video game project to put your knowledge into practice.

Stop wasting engineering hours on manual Jira maintenance. Learn how to combine deterministic Jira governance with targeted Atlassian Rovo AI agents to build a self-triage, friction-free delivery engine.