Artificial Intelligence

Learn how to build reliable AI agents with PydanticAI, from simple chatbots to production-ready multi-agent systems. With practical examples, clear explanations, and hands-on projects, this book helps you write AI applications that are structured, testable, and easy to maintain.

This book is a practical guide to building and running local AI systems in 2026. Learn how to choose hardware, run modern LLMs, build RAG pipelines and AI agents, and deploy secure, efficient infrastructure while keeping full control of your models and data.

Teams are producing more code than ever. Dashboards are green. And something is quietly breaking. AI coding assistants didn't break your development process; they revealed it was already fragile. Reimagine, Don't Retrofit is a field-tested argument that the software development lifecycle itself needs to be reimagined for the AI era, from governance and metrics to team roles and delivery flow.

An LLM is not an AI system.Systems Thinking for Agentic AI shows software engineers and architects how to design reliable AI applications with prompts, RAG, tools, memory, orchestration, guardrails, evaluation, observability, and runtime control.Move beyond chatbot demos and learn how to build production-ready agentic AI systems you can reason about, measure, debug, operate, and improve

Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable machine learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

Your use cases have a new reader: an AI agent that turns them directly into code and tests. Implementation is now cheap but expressing intent precisely is the bottleneck. This guide teaches you to write use cases that work for stakeholders, engineers, and the AI agents that implement from them.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

Everything you really need to know in Machine Learning in a hundred pages.

Design, Build, and Operate Autonomous AI Systems for Modern Cyber DefenseCybersecurity is evolving beyond traditional automation toward intelligent systems capable of continuously analyzing security telemetry, investigating threats, coordinating specialized tools, and executing controlled defensive actions. This shift introduces new engineering challenges involving agent architecture, decision-making, authorization, operational reliability, and the verification of security-critical actions.

Platform engineering is changing fast, and AI is raising the stakes. This practical guide shows you how to build internal developer platforms that work in the real world, from cloud-native foundations to AI-powered workloads. Follow hands-on examples and a production-grade reference architecture to create platforms that are secure, scalable and built to evolve.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

Stop copying code snippets into chat widgets. Master closed-loop autonomous AI coding agents, local offline LLMs (Ollama/Llama 3.1), event-driven lifecycle hooks, and Model Context Protocol (MCP) runtimes.

What happens when AI starts making the decisions behind your database queries? This practical guide explores how modern optimizers work, then shows how machine learning and reinforcement learning can improve them. Learn to build, test and deploy AI-driven optimization systems that deliver real performance gains.

Build Model Context Protocol servers that go beyond demos. This hands-on guide walks you from the fundamentals to production deployment with practical examples in Python, TypeScript, Go, Rust and Java. Learn the patterns, tools and real-world practices needed to build secure, scalable MCP servers that are ready for enterprise AI.