Books

Bad data breaks good code. You’ve written Python that works perfectly in testing, only to watch it fail in production because of a malformed API request, a messy CSV, or a missing config value. That’s the hidden cost of Python’s flexibility: without runtime validation, you’re always one bad input away from a crash. Enter Pydantic. This book takes you from the foundations of data validation to real-world applications in APIs, data pipelines, configurations, and machine learning workflows. Along the way, you’ll explore practical techniques, advanced features, and alternatives like Marshmallow, attrs, and dataclasses, so you’ll always know which tool is right for the job. If you’re a Python developer, data engineer, or FastAPI user, this is your roadmap to writing safer, cleaner, and more reliable code.

This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions. Also available at Google Play 

Learn how to build your own AI application step-by-step. A hands-on guide to AI development with local LLM inference

This book is a valuable resource for beginners and experienced Python developers, emphasizing that magic methods are not just syntactic sugar, but powerful tools that can significantly improve the functionality and performance of Python code when used correctly.

Create responsive front-end web applications in Python using the React and Material-UI JavaScript libraries, without having to program in JavaScript, by using the Transcrypt transpiler that turns your Python code into JavaScript.

Elegant Design Principles distils decades of design wisdom into 95 actionable principles spanning core OO, SOLID/GRASP, package design, reliability and a forward‑looking AI‑first approach. Explore the Design Pyramid to understand how quality attributes, smells and principles interconnect; learn to manage complexity through high cohesion, low coupling and clear abstractions; and adopt modern practices like test‑driven development and semantic modularity. From novices seeking a roadmap to experts embracing AI‑assisted workflows, this book equips you to create systems that are robust, maintainable and elegant—today and in the AI‑driven future.

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.

Quantitative finance in Python: a hands-on, interactive look at the QuantLib library through the use of Jupyter notebooks as working examples.

This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you’ve been seeking. This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions.

Logging is great for debugging and auditing. You'll learn how to log properly with Python in this book!

Learn how large language models work by building one from scratch. This hands-on guide walks you from first principles to a working Transformer you understand inside out.

Whether you know Go or not, if you use Kubernetes, you can extend it to automate all sort of administrative tasks. This book is going to take you from zero knowledge to building a basic operator in BASH shell, rewriting it Go and giving you enough material to extend Kubernetes using Python too!

Książka pomagająca wejść w świat programowania od języka Python. Zachęca i pokazuje jak uczyć się skutecznie.

This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. Many resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. Based on material taught at leading universities.