ml-ds: A Learning Resource Repository for Machine Learning and Artificial Intelligence

ml-ds is a personal learning repository maintained by GitHub user ajfm88, focusing on knowledge accumulation and practical exploration in the fields of machine learning and artificial intelligence. It covers practical content on topics like Agentic AI and SQL, and uses a modular organization approach to provide structured learning paths and reference value for learners at different stages.

• Author/Maintainer: ajfm88
• Source Platform: GitHub
• Original Repository Name: ml-ds
• Original Link: https://github.com/ajfm88/ml-ds
• Last Updated: June 14, 2026

This repository contains two main learning modules:

1. Agentic AI Crash Course

Agentic AI emphasizes autonomous decision-making and tool usage capabilities. This module covers core concepts, architectural design patterns, and application cases;

2. SQL Training Camp

SQL is an essential skill for data science. This module includes a complete learning path from basic queries to advanced optimization.

Learning Value:

• Real learning trajectory, recording experiences of overcoming challenges;
• Curated resource aggregation, saving search time;
• Continuous updates and iterations, keeping up with technological trends. Target Audience:
• Machine learning beginners;
• Developers who want to dive deep into Agentic AI;
• Data analysts/engineers needing to strengthen SQL skills;
• Self-learners looking for reference learning methods.

1. Fork and personalize: Adjust the learning order to fit your own needs;
2. Combine with practice: Hands-on reproduction of code examples;
3. Follow updates: Track dynamics via the Watch function;
4. Participate in contributions: Provide feedback on errors or resources via Pull Request.

In the era of information explosion, a reliable learning roadmap is crucial. The ml-ds repository focuses on core machine learning skills (from basic data query to Agentic AI technology), and its progressive approach is worth learning from, serving as one of the references for building a personal knowledge system.