Books

Master machine learning algorithms through worked examples and hands-on practice problems. From logistic regression to neural networks, this companion exercise book provides step-by-step solutions and progressive difficulty levels to build deep understanding.

Digital Transformation in the Era of AI reframes transformation as a continuous capability, not a one-time program. Drawing on real enterprise experience, this book shows how organisations can evolve their architectures, operating models, and leadership practices to harness AI responsibly and at scale, without breaking what already works.

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

Bridge the gap between HYSYS simulations and industrial reality using Physics-Informed AI and MATLAB

Machine learning doesn’t fail in theory—it fails in production. This book shows you how to build PyTorch systems that remain robust when data shifts, assumptions break, and reliability matters.

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 :)

Most AI systems fail in production. They chain prompts together and call it "agentic". They collapse under real-world pressure. The gap isn't the technology – it's missing infrastructure. This book bridges that gap. Learn the architecture patterns and infrastructure to build autonomous AI systems that work at scale. Master what distinguishes real agents from chatbots with tools. Deploy with confidence. Stop building demos. Start shipping production agentic AI.