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 a local, privacy-first tool-calling agent using Google's Gemma 4 model family (specifically the 2B edge variant) and Ollama. The tutorial covers the concept of tool/function calling in LLMs, setting up Ollama with gemma4:e2b, and implementing four real-world tools in Python (weather, news, time, currency conversion) using only standard library dependencies. The agent is tested with multi-tool queries, demonstrating reliable structured JSON output and function dispatch entirely offline on consumer hardware.

How to Implement Tool Calling with Gemma 4 and Python

More Tools: Expanding the Tool Calling Capabilities