Books

A practical field guide for using AI assistants at work—and engineering them in production. Learn the standards that prevent “sounds right, wrong” outputs: specs, grounding, tools, evals, guardrails, and cost control.

A hands-on guide for PHP developers who want to use AI and machine learning in real projects. No hype, no math—just practical ideas, tools, and PHP code that works.

What if AI didn’t just answer questions, but actually did the work? This book takes you from basic AI concepts to autonomous agents that can reason, plan, and act. No hype. No heavy math. Just clear explanations, real examples, and a builder’s mindset.

Are you interested in starting or returning to Machine Learning? This book offers a concise, straightforward view into the field and fundamental techniques that see use even in high-level career roles. Through a combination of examples, math and coding projects, the book will grow the reader's confidence in being a Machine Learning practitioner.

Building generative AI application is not only about LLM choice and prompt engineering, but also about the well-architected cloud solution.

Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

Build a real AI platform on OpenShift, not just “another Kubernetes cluster.” This guide walks you through air-gapped installs, Quay mirroring, GPUs, InfiniBand, GitOps, and benchmarking—so platform and SRE teams can deliver a secure, observable, high-performance OpenShift AI environment that app teams actually want to use.

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

PLEASE NOTE THAT THIS IS NOT THE FULL BOOK BUT JUST CHAPTER 1 and 2 because a simple topic like Simple Linear Regression became 20+ pages long. As a full-time frontend architect, I'm writing and releasing this book chapter by chapter, at a pace that fits around my day job and life. So, think of this as a journey we're taking together, one concept at a time. REST OF THE CHAPTERS WILL BE AVAILABLE AT REGULAR INTERVALS. So keep me motivated by subscribing :)

Discover how modern web scraping goes beyond simple scripts. Learn to extract, automate, and transform data from dynamic websites using Python, Playwright, undetected-chromedriver, Chrome DevTools MCP, and AI tools. Packed with hands-on examples and expert tips, this book shows you how to build ethical, scalable, and intelligent scraping workflows that turn raw web content into actionable insights.

This book presents a modern approach to system identification and adaptive control through the lens of online machine learning. It bridges theory and practice, guiding readers from classical linear control to advanced nonlinear adaptive methods with MATLAB examples. Designed for students, researchers, and engineers, it provides the knowledge and tools to design intelligent control systems for real-world applications.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

A model that wins on MAPE can lose money on the shelf. A 15% accuracy gain can hide a 12% bias. An interval that looks tight can be wrong a third of the time.Forecast metrics are decision instruments, not neutral truth detectors — and most teams are still choosing them by habit. This book shows where MAE, MAPE, sMAPE, WAPE, MASE, RMSSE, CRPS, pinball loss, energy scores, bias measures and cost-based metrics help, and where they quietly mislead.Twelve chapters take you from elicitation and baselines through intermittent demand, probabilistic and hierarchical scoring, and the accuracy–utility gap, to a production evaluation system with monitoring, drift detection, alerting and governance. Every metric arrives with its assumptions, every criticism carries a remedy, and a retrospective Walmart M5 case study runs throughout, with Python listings and reproducibility checks.For demand planners, forecasting data scientists, ML engineers and analytics leads who need evaluation choices they can defend. Measure what matters.

So how do you make an AI agent and conversational agent understand time? How does time shape attention? How is time important for the context engine? You will learn how to add time to knowledge graphs, how time and causality drive context, and how to make the knowledge graphs that are used for AI memory time-aware.

Helping C# and .NET developers to learn how to do machine learning and become highly sought-after (and well-paid) AI engineers. No prior experience of ML required!