Books

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.

800 pages. 11 chapters. The full forecasting stack in Python — from ARIMA to foundation models — with production-grade code and proper evaluation. No hype.

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.

Unlock the full potential of CatBoost — a powerful gradient boosting library built for structured/tabular data and still underused in practice.In Mastering CatBoost: The Hidden Gem of Tabular AI, you’ll learn how to take advantage of CatBoost’s key strengths: native categorical feature handling, strong accuracy, and fast inference — without brittle preprocessing pipelines.Written for data scientists, ML engineers, and applied researchers, the book covers:Real-world use cases and end-to-end workflowsPractical tuning strategies and diagnosticsInterpretability with SHAP, feature importance, and constraintsDeployment-minded best practices and common failure modesWhether you’re new to CatBoost or ready to go deeper, this is a clear, practical guide to building high-performance tabular models.Early Access is available now, with ongoing updates leading to the full release in 2026.

AI engineering is becoming one of the most valuable and in-demand areas of software development, and Python, LangChain, and LangGraph are core skills for building the systems behind it. Learn how to turn basic Python knowledge into production-grade backend and agentic AI applications—and move toward an engineering niche centered on building and controlling AI rather than competing with it.

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.

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.

A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (492 manuscript pages).

Python looks the same on the surface, but almost everything underneath has changed. This book brings you up to speed on Python 3.13/3.14, the free threaded build, uv, and the tools working engineers actually use today, with runnable examples in every chapter.

The Databricks platform and data-engineering playbook for the engineers who own pipelines, govern catalogs, and keep workloads on schedule. Sixteen chapters on Unity Catalog, Lakeflow, identity, observability, and performance. Azure examples; concepts mapped to AWS and GCP.

A model that crashes is a good day. The dangerous failures return answers — plausible, fluent, and wrong. 100 failures. 63 diagnostic instruments. One rule: inspect what actually happened.

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.

Regex doesn't have to feel like cryptic punctuation. Mastering Regular Expressions shows you how patterns really work, how to debug them when they don't and how to use them confidently across nine programming languages. Go beyond memorizing syntax and learn to write regex that's clear, efficient and portable.