Books

AI made creation cheaper. It also made judgment more valuable.Taste: Turning Vibe into Assets in the AI Age shows why the next wave of winners will not simply be the people with the best credentials or the biggest teams, but the ones who can turn instinct, clarity, and initiative into real assets.

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.

Master the art of AI interaction with 22 proven prompting techniques, real code examples, and production-tested strategies. From the creator of GitHub's most-starred prompt engineering repository (7,100+ stars).

Stop guessing which machine learning algorithm to use. This book provides clear mathematical explanations, decision frameworks, and real-world examples to help you select, implement, and evaluate models correctly from data to deployment.

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

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

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.