Infra AI Agent Tutorials — AI skill for Claude Code

AI Agent Development Tutorial

Overview

This repository provides a comprehensive tutorial for developing AI agents, covering the complete spectrum from machine learning foundations to advanced AI agent systems. The tutorial takes a hands-on approach, guiding learners through practical implementations of machine learning, deep learning, natural language processing, large language models, retrieval-augmented generation, and AI agent development. Each module includes Jupyter notebooks, Python scripts, and real-world examples designed for progressive skill building.

Project Structure

The tutorial is organized into eight main modules, each focusing on specific aspects of AI agent development:

• 01_prepare: Initial planning and survey materials
• 02_setup: Environment configuration and dependency management
• 03_ML: Machine learning fundamentals with PyTorch and Keras
• 04_DL_foundation: Deep learning core concepts including forward propagation, gradient descent, optimization, loss functions, activation functions, data augmentation, and normalization
• 05_NLP: Natural language processing covering tokenization, embeddings, similarity measures, N-grams, BLEU scores, sentiment analysis, RNN architectures, and CLIP image-to-text models
• 06_LLM: Large language model fine-tuning with Gemma, Llama3, BERT, and Chain-of-Thought training
• 07_RAG: Retrieval-augmented generation using LangChain, prompt templates, LCEL, function calling, agents, database integration, and web scraping
• 08_AI_Agent: Complete AI agent development including chatbots, Ollama integration, agent frameworks, LLM-MCP applications, and infrastructure graph RAG systems

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