Learn to build reliable AI agents in Python without wrestling messy LLM outputs. Use Pydantic AI for structured outputs, tool calling, and clean dependency injection.

Machine Learning Mastery offers developers resources and tutorials on machine learning algorithms, techniques, and applications. Developers can learn about supervised and unsupervised learning methods, deep learning frameworks, and practical machine learning projects. Additionally, the blog covers topics such as data preprocessing, model evaluation, and hyperparameter tuning, providing  insights for both beginners and experienced practitioners in the field of machine learning.

Machine Learning Mastery

A hands-on guide to building production-ready AI agents in Python using Pydantic AI. Covers defining Pydantic models for type-safe structured outputs, registering Python functions as agent tools with docstring-driven dispatch, injecting runtime dependencies (database connections, API clients) via a typed RunContext, and using built-in capabilities like web search and extended thinking. Includes code examples for each concept and a GitHub Colab notebook for reference.

Building AI Agents in Python with Pydantic AI

Building Your First Agent with Pydantic AI

Using Built-in Capabilities in Pydantic AI