Artificial Intelligence with Python Cookbook

Practical AI recipes in Python

Artificial Intelligence with Python Cookbook is built for readers who want to do real work with AI, not just read about it. Ben Auffarth presents a recipe-based approach to machine learning and deep learning, moving from environment setup and data preparation into modeling, evaluation, and more advanced AI techniques.

The book starts with the practical foundations: getting a Python environment ready, working in Jupyter, and building confidence with the tools that support day-to-day AI development. From there, it widens into supervised learning, feature engineering, clustering, anomaly detection, recommendation systems, probabilistic modeling, logical inference, and heuristic search.

A broad tour of modern AI methods

This is a wide-ranging technical cookbook that brings together several major parts of the AI toolkit. Readers encounter scikit-learn, Keras, PyTorch, and TensorFlow-based workflows, along with topics such as reinforcement learning, deep learning applications, and production-minded AI use cases.

The format makes it easy to dip into a specific problem area, while still building a larger picture of how different methods fit together. That makes the book especially appealing to developers, data scientists, machine learning engineers, and technically minded readers who prefer learning by example.

What makes it useful

• Step-by-step recipes for common and advanced AI tasks
• Coverage that spans classical machine learning and deep learning
• Practical examples in Python using widely used libraries
• A structure that supports both browsing and focused study

Who will appreciate it

If you like books that move quickly from concept to implementation, this title fits that style well. It is best suited to readers who already have some Python and machine learning familiarity and want to extend their toolkit with a broad set of applied AI techniques.