Python
Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.
- Mastering Advanced Time Series Forecasting in Python: Probabilistic, Hierarchical, and Foundation ModelsMaster advanced forecasting with Python using machine learning, deep learning, and cutting-edge foundational models. Learn hierarchical and probabilistic forecasting, forecastability, metrics, and scalable pipelines. Build robust, real-world forecasting systems with production-ready code and expert guidance.Valery Manokhin
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.
- Django 6 Cookbook, Second EditionBuild modern full-stack apps with Django 6, Python 3.12, APIs, authentication, testing, search, and deploymentGitforGits | Asian Publishing House
The recipes in this book are practical answers to the kind of problems that real Django applications encounter, sometimes on the first day of a project and sometimes deep into the life of a codebase that has grown well beyond its original design. This book is written to guide you to utilize Django 6.0 capabilities in your apps with ease of implementation.
- Discrete Mathematics for Computer ScienceAlexander S. Kulikov, Alexander Golovnev, Alexander Shen, Vladimir Podolskii, and Marie Brodsky
This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.
- Probabilistic Forecasting with Conformal Prediction in PythonThe Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and ForecastingValery Manokhin
Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.
Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí
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.
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.
- Hermes Agent: The Self-Evolving AI WorkforceEdgar MilvusNo Description Available
Unlock the power of DuckDB for modern geospatial analytics. This hands-on guide helps GIS professionals master efficient spatial data management, transforming massive real-world datasets into powerful insights using SQL, Python, and DuckDB’s spatial extension. Full-color print edition is available on Amazon.
- PyTorch Deep DiveFrom Foundations to Production: A Complete Guide to Modern Deep LearningSteve Publications
PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.
- Local AI Engineering with OllamaRun, understand, customize, fine-tune, and build agentic apps on your own hardwareAymen El Amri
Pull a model onto a machine you own, shape it with a Modelfile, fine-tune your own adapter, and build a chat app that calls tools and talks to an MCP server, all running on your own hardware. By the end, you'll know exactly where owning your AI beats renting it, and where it doesn't.
- Simplifying Machine Learning with PyCaretA Low-code Approach for Beginners and Experts!Giannis Tolios
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!
- Creating TUI Applications with Textual and PythonMichael Driscoll
Learn how to create amazing lightweight user interfaces using Python and Textual in your terminal! You will learn the basics of Textual and then create ten different applications.
- Interpreting Machine Learning Models With SHAPA Guide With Python Examples And Theory On Shapley ValuesChristoph Molnar
Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.