🧠 LLM-Learning

A Comprehensive Journey into Large Language Models, Generative AI & Autonomous Agents

🚀 939+ Notebooks · 📦 428 Python Scripts · 📚 5 Learning Tracks · 🤖 8+ AI Frameworks

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From LLM fundamentals to production-grade autonomous agents — everything in one repository.

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📑 Table of Contents

• 🌟 Overview
• 📂 Repository Structure
• 📓 1 — My Notebooks (Personal Notes)
• 🎓 3 — Generative AI Course
• ⚙️ 4 — LLM Engineering (8-Week Bootcamp)
• 🔍 5 — LLM RAG (Work in Progress)
• 🤖 6 — AI Agents
• 🛠️ Technologies & Frameworks
• 🚀 Getting Started
• 📊 Repository Stats

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🌟 Overview

This repository is a structured, hands-on learning hub covering the full spectrum of modern AI engineering — from the theory behind Large Language Models to building production-grade RAG systems and autonomous multi-agent workflows.

📚 Learning Progression
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  LLM Basics ──▶ Prompt Engineering ──▶ Open-Source Models
       │                                       │
       ▼                                       ▼
  RAG Systems ──▶ Fine-Tuning (Closed + Open) ──▶ Agentic AI
       │                                            │
       ▼                                            ▼
  Vector DBs ──▶ Evaluation & Benchmarks ──▶ Multi-Agent Systems

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📂 Repository Structure

LLM-Learning/
│
├── 📓 1_MyNotebooks/           # Personal consolidated notes & projects
│   ├── 1_LLM/                  # Core LLM concepts (13 notebooks)
│   ├── 2_RAG/                  # RAG fundamentals (3 notebooks)
│   ├── 3_AgentAI/              # Agent architecture (1 notebook)
│   └── 4_Projects/             # Practical projects (1 notebook)
│
├── 🎓 3_Generative_AI/         # GenAI, ChatGPT & Copilot course (19 modules)
│   ├── Module 0 – Module 18    # Data wrangling, EDA, visualization
│   └── Course Outline.xlsx     # Full course syllabus
│
├── ⚙️ 4_LLM_Engineering/       # 8-week LLM Engineering bootcamp (900+ notebooks)
│   ├── week1/                  # Web scraping & summarization
│   ├── week2_Gradio_And_ChatBot/
│   ├── week3_HuggingFace_OpenSource_Solutions/
│   ├── week4_Model_Evaluation/
│   ├── week5_RAG/
│   ├── week6_FineTuneing_ClosedSourceLLM/
│   ├── week7_FineTuning_OpenSource/
│   ├── week8_AgentAI/
│   ├── guides/                 # 14 foundational guides
│   ├── community-contributions/
│   └── extras/
│
├── 🔍 5_LLM_RAG/               # RAG deep-dive (in progress)
│
└── 🤖 6_AI_Agents/             # Agent frameworks & courses
    ├── Deeplearning.ai_AutoGen/
    ├── Deeplearning.ai_Crew.ai/
    ├── Google_Kaggle/
    └── HuggingFace/

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📓 1 — My Notebooks (Personal Notes)

Consolidated, self-authored notes covering LLM fundamentals, RAG, Agents, and hands-on projects.

🔷 LLM Fundamentals (1_MyNotebooks/1_LLM/)

#	Notebook	Topic
0	📝 Questions	Key questions & interview prep
1	📘 Basics	LLM fundamentals, models, tokenization
2	🔧 Setup LLM	Environment setup & configuration
3.1	⚡ LLM Engineering	Prompt engineering & API usage
3.2	🎯 Choosing a Model	Model selection & comparison
4.1	🦙 Model Internals — LLAMA	Deep dive into LLAMA architecture
5.1	🤗 HuggingFace Library	HuggingFace Transformers integration
6.1	🏋️ Training	Fine-tuning & training concepts
9.1	💼 Business Implementation	Real-world enterprise use cases
9.2	💰 Commercial (Theory)	Commercial LLM landscape
10.1	☁️ Inference on Cloud	Cloud deployment strategies
10.2	🛒 Amazon Price Agent	Price estimation agent project

🔷 RAG — Retrieval-Augmented Generation (1_MyNotebooks/2_RAG/)

#	Notebook	Topic
1	📘 Basics	RAG fundamentals, vector stores, embeddings
2	🔄 RAG Workflow	End-to-end RAG pipeline
3	🦜 LangChain	LangChain library deep-dive

🔷 Agent AI (1_MyNotebooks/3_AgentAI/)

#	Notebook	Topic
1	🤖 Basics	Agent architecture & frameworks

🔷 Projects (1_MyNotebooks/4_Projects/)

#	Notebook	Topic
1	💬 Self-Twin ChatBot	Personal AI chatbot project

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🎓 3 — Generative AI Course

GenAI, ChatGPT, CoPilot & Agents for Business — A comprehensive 19-module course covering data science foundations and generative AI applications.

📊 Notebooks

Module	Notebook	Topic
3	📊 Data Wrangling (Stemplicity)	Data wrangling & analysis with Pandas
3	🔍 EDA with Pandas	Exploratory data analysis project
4	📈 Data Visualization	Data visualization with Seaborn

📋 Full course outline: GenAI, ChatGPT, CoPilot & Agents for Business - Course Outline.xlsx

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⚙️ 4 — LLM Engineering (8-Week Bootcamp)

900+ notebooks across an intensive 8-week program progressing from web scraping to autonomous agents. Includes community contributions from 40+ learners.

📅 Week 1 — Web Scraping & Summarization

Notebook	Topic
📄 Day 1	Web scraping fundamentals
📄 Day 2	Text summarization with LLMs
📄 Day 4	Content generation techniques
📄 Day 5	Brochure generation project
📝 Week 1 Exercise	Weekly challenge

📅 Week 2 — Gradio & ChatBot Development

Notebook	Topic
💬 Day 1	Gradio UI basics
💬 Day 2	Building chatbots
💬 Day 3	Multi-model chat interfaces
💬 Day 4	Advanced Gradio features
📝 Week 2 Exercise	Weekly challenge

📅 Week 3 — HuggingFace & Open-Source Solutions

Notebook	Topic
🤗 Day 1	HuggingFace ecosystem introduction
🤗 Day 2	Pipelines API
🤗 Day 2 — Pipelines	HuggingFace Pipelines deep-dive
🤗 Day 3	Tokenizers
🤗 Day 3 — Tokenizers	Tokenizer internals
🤗 Day 4	Models & inference
🤗 Day 4 — Models	Model loading & configuration
🤗 Day 5	Meeting minutes product
🤗 Day 5 — Meeting Minutes	Meeting minutes generator

📅 Week 4 — Model Evaluation & Code Generation

Notebook	Topic
📊 Day 3	Code language conversion
📊 Day 4	Open-source code conversion
📊 Day 5	Evaluation & benchmarking

📅 Week 5 — RAG (Retrieval-Augmented Generation)

Notebook	Topic
🔍 Day 1	RAG core concepts
🔍 Day 2	Document chunking strategies
🔍 Day 3	Basic RAG implementation
🔍 Day 4	RAG testing & quality
🔍 Day 5	RAG without LangChain

📅 Week 6 — Fine-Tuning Closed-Source LLMs

Notebook	Topic
🔥 Day 1	Fine-tuning fundamentals
🔥 Day 2	Data preprocessing
🔥 Day 3	Machine learning foundations
🔥 Day 4	Neural network & LLM training
🔥 Day 5	LLM fine-tuning with OpenAI
📈 Results	Training results analysis
🔁 Redemption Train	Improved training run
🔁 Redemption Run	Improved inference run

📅 Week 7 — Fine-Tuning Open-Source Models

Notebook	Topic
🦙 Day 1	Open-source fine-tuning intro
🦙 Day 1 — QLoRA Memory	QLoRA & memory optimization
🦙 Day 2	Base model evaluation
🦙 Day 2 — OS Evaluation	Open-source model benchmarking
🦙 Day 3 & 4	Training with PEFT/LoRA
🦙 Day 3 — Training	Full training pipeline
🦙 Day 5	Testing fine-tuned models
🦙 Day 5 — Testing	Fine-tuned model evaluation
📈 Results	Training results

📅 Week 8 — Autonomous Agent AI

Notebook	Topic
🤖 Day 1	Agent fundamentals
🤖 Day 2	Tool usage & function calling
🤖 Day 3	Planning & reasoning
🤖 Day 4	Multi-agent orchestration
🤖 Day 5	Production deployment
📈 Results	Agent performance analysis

📖 Foundational Guides

#	Guide	Topic
01	🚀 Intro	Course introduction
02	💻 Command Line	Terminal & CLI basics
03	🔀 Git & GitHub	Version control essentials
04	🏗️ Technical Foundations	CS fundamentals for AI
05	📓 Notebooks	Jupyter notebook mastery
06	🐍 Python Foundations	Python for AI/ML
07	✨ Vibe Coding & Debugging	AI-assisted development
08	🐛 Debugging	Debugging strategies
09	🔌 AI APIs & Ollama	API setup & local models
10	🐍 Intermediate Python	Advanced Python patterns
11	⚡ Async Python	Asynchronous programming
12	🎯 Starting Your Project	Project scaffolding
13	🌐 Frontend	Frontend for AI apps
14	🐳 Docker & Terraform	Containerization & IaC

🌍 Community Contributions & Extras

Resource	Description
📂 Community Contributions	87+ notebooks from 40+ contributors
📂 Extras — Signal Prototype	Signal processing prototype
📂 Extras — Trading Prototype	Trading system prototype

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🔍 5 — LLM RAG (Work in Progress)

🚧 This section is under active development — Deep-dive into advanced RAG patterns and techniques.

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🤖 6 — AI Agents

Hands-on courses from DeepLearning.AI, Google/Kaggle, and HuggingFace covering leading agent frameworks.

🔵 AutoGen — Multi-Agent Conversations (Deeplearning.ai_AutoGen/)

Lesson	Notebook	Topic
L1	🎭 Multi-Agent Conversation	Multi-agent conversations & stand-up comedy
L2	🔗 Sequential Chats	Sequential chats & customer onboarding
L3	🪞 Reflection	Reflection & blog post writing
L4	♟️ Tool Use	Tool use & conversational chess
L5	💹 Coding & Finance	Coding & financial analysis
L6	📋 Planning	Planning & stock report generation

🟣 Crew.ai — Multi-Agent Orchestration (Deeplearning.ai_Crew.ai/)

Lesson	Notebook	Topic
L0	📘 Basics	Crew.ai fundamentals
L2	📰 Research & Articles	Research & article writing agents
L3	🎧 Customer Support	Customer support automation
L4	📞 Customer Outreach	Tools & customer outreach
L4	📊 Sales Pipeline	Sales pipeline management
L5	🎉 Event Planning	Task-based event planning
L5	📋 Support Reports	Executive support reports
L6	💰 Financial Analysis	Collaborative financial analysis
L6	📝 Content at Scale	Content creation at scale
L7	📄 Job Application	Job application crew
L11	🗂️ Project Planning	Project planning agents

🟢 Google Kaggle — Agent Fundamentals (Google_Kaggle/)

Notebook	Topic
🤖 Basic Agents	Introduction to AI agents

🟡 HuggingFace (HuggingFace/)

Resource	Description
🏆 Certificate	Course completion certificate

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🛠️ Technologies & Frameworks

Category	Technologies
🧠 LLM APIs	OpenAI (GPT), Anthropic (Claude), Google (Gemini)
🦙 Open-Source Models	LLaMA, Mistral, DeepSeek, Qwen
🤗 ML Platform	HuggingFace Transformers, Datasets, PEFT
🦜 Orchestration	LangChain, LiteLLM
🤖 Agent Frameworks	AutoGen, Crew.ai, Custom agents
💾 Vector Databases	ChromaDB, Pinecone, Qdrant, FAISS
🖥️ UI Frameworks	Gradio, Streamlit
🏋️ Fine-Tuning	QLoRA, PEFT, LoRA, OpenAI Fine-tuning API
☁️ Cloud & Deploy	Google Colab, Modal, Docker, Terraform
🐍 Languages	Python, C/C++, JavaScript
🔧 Dev Tools	Jupyter, Ollama, uv, Cursor

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Jupyter Notebook / JupyterLab
• API keys for OpenAI / Anthropic / Google (optional — free alternatives available)

Quick Start

# Clone the repository
git clone https://github.com/SouravAggarwal/LLM-Learning.git
cd LLM-Learning

# Start with personal notes
jupyter notebook 1_MyNotebooks/1_LLM/1_Basics.ipynb

# Or jump into the LLM Engineering bootcamp
cd 4_LLM_Engineering
pip install -r requirements.txt
jupyter notebook week1/day1.ipynb

🏠 Run Local Models with Ollama

# Install Ollama from https://ollama.com
ollama run llama3.2         # Standard (3B parameters)
ollama run llama3.2:1b      # Lightweight for smaller machines

💡 Tip: See Guide 09 — AI APIs & Ollama for detailed setup with Ollama, Gemini, OpenRouter, and more free alternatives.

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📊 Repository Stats

Metric	Count
📓 Jupyter Notebooks	939+
🐍 Python Scripts	428
📖 Markdown Docs	208
📁 Total Files	1,600+
📅 Weekly Modules	8 weeks
🏗️ Projects	100+ community projects
👥 Contributors	40+
🤖 AI Frameworks	8+

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