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This repository contains implementations of Retrieval-Augmented Generation (RAG) in Jupyter notebooks. It includes examples of building chatbots with and without history, processing PDFs with RAG, and using DeepSeek models for local RAG and financial document analysis.

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πŸš€ Complete AI & Machine Learning Course Collection

KGP Talkie

AI Development LangChain Machine Learning Students

Master AI Development β€’ Build Production Applications β€’ Deploy at Scale

πŸ€– LLM Development β€’ πŸ“Š Machine Learning β€’ πŸš€ Production


πŸ€– LLM and AI Agent Development

🎯 Master OpenAI Agent Builder - Low-Code AI Projects Workflow

🎯 Build and deploy AI agents visually using OpenAI Agent Builder, ChatKit, RAG, Chatbot, AI Assistant with MCP, AWS, RDS MySQL

What You'll Master:

  • βœ… Visual AI Development: Build AI agents without complex coding using OpenAI Agent Builder
  • βœ… Real-World Integration: Connect AI workflows with MySQL, AWS, and MCP connectors
  • βœ… Production Deployment: Deploy AI agents with ChatKit and Guardrails for safety
  • βœ… Complete Projects: Weather Agent, RAG Document Q&A Chatbot, E-Commerce AI Assistant
  • βœ… Database Integration: AWS RDS MySQL connection and management
  • βœ… Cloud Deployment: AWS Lambda and API Gateway for production use

🎯 Technologies: OpenAI Agent Builder β€’ ChatKit β€’ AWS β€’ RDS MySQL β€’ MCP β€’ Lambda β€’ API Gateway

πŸ”₯ MCP Mastery: Build AI Apps with Claude, LangChain and Ollama

🎯 Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations

What You'll Master:

  • βœ… MCP Architecture: Client, server, and transport layers
  • βœ… Claude Desktop Integration: Direct MCP server connections
  • βœ… Real-World Applications: Data analysis servers for Excel, PowerPoint, SQLite
  • βœ… RAG Implementation: Vector databases with LangChain integration
  • βœ… Production Deployment: Testing, security, and cloud deployment

🎯 Technologies: Python β€’ Streamlit β€’ ChromaDB β€’ LangChain β€’ LangGraph β€’ Ollama

πŸ“Š Agentic RAG with LangChain and LangGraph - Ollama

🎯 Step-by-Step Guide to RAG with LangChain, LangGraph, and Ollama | DeepSeek R1, QWEN, LLAMA, FAISS

Advanced RAG Techniques:

  • 🧠 Agentic RAG: Intelligent, adaptive systems that act like smart assistants
  • πŸ”„ Corrective RAG: Self-improving and error-correcting mechanisms
  • πŸ“Š Document Processing: Doclings integration for seamless document loading
  • πŸš€ Production Ready: Streamlit apps and AWS EC2 deployment

Technologies: LangChain β€’ LangGraph β€’ Ollama β€’ DeepSeek R1 β€’ QWEN β€’ LLAMA β€’ FAISS

πŸ”§ Master LangGraph and LangChain with Ollama

🎯 Agentic RAG and Chatbot, AI Agent, DeepSeek, LLAMA 3.2 Agent, FAISS Vector Database

Build Production Chatbots:

  • πŸ’¬ Memory-Enabled Chatbots: Dynamic conversations with persistent memory
  • πŸ—„οΈ Database Integration: Seamless MySQL query execution with LLMs
  • πŸ“ˆ State Management: LangGraph workflows with advanced state machines
  • 🎯 Private Data RAG: Custom embeddings and vector database integration

Technologies: LangGraph β€’ LangChain β€’ Ollama β€’ DeepSeek β€’ LLAMA 3.2 β€’ MySQL β€’ FAISS

⚑ Master Langchain and Ollama - Chatbot, RAG and Agents

🎯 Master Langchain v0.3, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide

Complete LangChain Journey:

  • πŸ› οΈ Setup & Integration: Professional Ollama and Langchain configuration
  • πŸ’¬ Custom Chatbots: Memory, history, and advanced features with Streamlit
  • ⛓️ Prompt Engineering: Templates, chains (Sequential, Parallel, Router)
  • πŸ€– Agent Development: Custom tools and step-by-step instruction execution
  • πŸš€ AWS Deployment: Production-ready applications on AWS EC2

Technologies: Langchain v0.3 β€’ Ollama β€’ DeepSeek β€’ LLAMA 3.2 β€’ Streamlit β€’ AWS EC2

πŸ”¬ Fine Tuning LLM with Hugging Face Transformers for NLP

🎯 Learn transformer architecture fundamentals and fine-tune LLMs with custom datasets

Advanced LLM Customization:

  • 🧠 Transformer Deep Dive: Architecture fundamentals and mathematical foundations
  • πŸ“Š Custom Dataset Preparation: Data preprocessing and formatting techniques
  • ⚑ Fine-tuning Mastery: Advanced optimization and training strategies
  • 🎯 Model Optimization: Performance tuning and evaluation methodologies

Technologies: Hugging Face Transformers β€’ PyTorch β€’ Custom Datasets β€’ Advanced NLP


πŸ“Š Machine Learning and Data Science

🧠 Deep Learning for Beginners with Python

🎯 Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Transfer Learning and Much More

Complete Neural Network Mastery:

  • πŸ”— Artificial Neural Networks (ANN): Build from mathematical foundations
  • πŸ‘οΈ Convolutional Neural Networks (CNN): Image processing and computer vision
  • πŸ”„ Recurrent Neural Networks (RNN): Sequential data and time series analysis
  • πŸ“ LSTM Networks: Advanced sequence modeling and memory networks
  • πŸ”„ Transfer Learning: Leverage pre-trained models for custom applications

Technologies: Python β€’ TensorFlow β€’ Keras β€’ Neural Network Architectures β€’ Computer Vision

πŸš€ Advanced Machine Learning and Deep Learning Projects

🎯 Build advanced projects using transformer models like BERT, GPT-2, and XLNet

Cutting-Edge Project Portfolio:

  • πŸ€– BERT Implementation: Natural language understanding and classification
  • πŸ’­ GPT-2 Applications: Text generation and completion systems
  • ⚑ XLNet Techniques: Bidirectional language modeling
  • 🎯 Multi-modal AI: Combine text, image, and audio processing
  • πŸ”§ Custom Architectures: Design and implement specialized models

Technologies: BERT β€’ GPT-2 β€’ XLNet β€’ Advanced Transformers β€’ Multi-modal AI

πŸ“ˆ Python for Linear Regression in Machine Learning

🎯 Master statistical foundations and practical implementation of regression analysis

Statistical Mastery:

  • πŸ“Š Regression Theory: Mathematical foundations and statistical principles
  • πŸ“ˆ Hypothesis Testing: Statistical validation and significance testing
  • πŸ”’ Feature Engineering: Variable selection and transformation techniques
  • 🎯 Model Evaluation: R-squared, RMSE, and comprehensive diagnostics
  • πŸ’Ό Business Applications: Real-world predictive modeling scenarios

Technologies: Python β€’ Scikit-Learn β€’ Statistical Analysis β€’ Pandas β€’ NumPy

🎯 Machine Learning & Data Science for Beginners in Python

🎯 Complete foundation in ML and DL using Python, Scikit-Learn, Keras, and TensorFlow

Complete Foundation:

  • 🐍 Python for Data Science: From basics to advanced data manipulation
  • πŸ“Š Data Analysis Mastery: Pandas, NumPy, and exploratory data analysis
  • πŸ€– Machine Learning: Supervised and unsupervised learning algorithms
  • 🧠 Deep Learning Introduction: Neural networks with Keras and TensorFlow
  • πŸ“ˆ Data Visualization: Professional charts and insights presentation

Technologies: Python β€’ Scikit-Learn β€’ Pandas β€’ NumPy β€’ Matplotlib β€’ TensorFlow

πŸ’¬ Natural Language Processing in Python for Beginners

🎯 Build NLP models using Python with Spacy, NLTK, and modern NLP techniques

NLP Expertise:

  • πŸ”€ Text Processing: Spacy and NLTK for production-ready NLP
  • πŸ“Š Sentiment Analysis: Emotion detection and opinion mining
  • 🏷️ Named Entity Recognition: Extract people, places, organizations
  • πŸ” Text Classification: Document categorization and content analysis
  • 🎯 Feature Engineering: TF-IDF, word embeddings, and advanced features

Technologies: Python β€’ Spacy β€’ NLTK β€’ NLP Pipelines β€’ Text Analytics


πŸš€ Production and Deployment

🌐 Deploy ML Model in Production with FastAPI and Docker

🎯 Professional deployment strategies using FastAPI, Docker, and modern DevOps practices

Production Deployment Mastery:

  • 🌐 FastAPI Development: High-performance API creation for ML models
  • 🐳 Docker Containerization: Scalable and portable deployment solutions
  • ☁️ Cloud Deployment: AWS, GCP, and Azure deployment strategies
  • πŸ”’ Security & Monitoring: Authentication, logging, and performance monitoring
  • ⚑ DevOps Integration: CI/CD pipelines and automated deployment

Technologies: FastAPI β€’ Docker β€’ Cloud Platforms β€’ DevOps β€’ Production Security

πŸ“Š Data Visualization in Python Masterclass for Beginners

🎯 Professional visualization and dashboard development using modern Python libraries

Visualization Excellence:

  • πŸ“ˆ Matplotlib Mastery: Static plots with professional customizations
  • 🎨 Seaborn Styling: Statistical visualizations and advanced aesthetics
  • ⚑ Plotly Interactive: Dynamic charts and real-time dashboards
  • πŸ“Š Dashboard Development: Streamlit and Dash applications
  • πŸ’Ό Business Intelligence: Professional reporting and data storytelling

Technologies: Matplotlib β€’ Seaborn β€’ Plotly β€’ Streamlit β€’ Dash β€’ Business Analytics


πŸ› οΈ Technologies & Frameworks Covered

πŸ”§ Complete Technology Stack

Python LangChain TensorFlow PyTorch FastAPI

🎯 Specialized Technologies

πŸ€– AI & LLM

  • OpenAI Agent Builder
  • LangChain
  • LangGraph
  • Ollama
  • Hugging Face Transformers
  • OpenAI API
  • Claude API

πŸ“Š ML & Data Science

  • Scikit-Learn
  • TensorFlow & Keras
  • PyTorch
  • Pandas & NumPy
  • Matplotlib & Seaborn
  • Plotly

πŸš€ Deployment & Production

  • FastAPI
  • Streamlit
  • Docker
  • AWS EC2
  • AWS Lambda
  • API Gateway
  • Vector Databases (FAISS, ChromaDB)
  • MySQL Integration

🎯 Learning Path Recommendations

πŸ€– AI/LLM Developer Path

Master OpenAI Agent Builder - Low-Code AI Projects Workflow
↓
Master Langchain and Ollama - Chatbot, RAG and Agents
↓
Master LangGraph and LangChain with Ollama
↓
Agentic RAG with LangChain and LangGraph - Ollama
↓
MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
↓
Fine Tuning LLM with Hugging Face Transformers for NLP

πŸ“Š Data Scientist Path

Python for Linear Regression in Machine Learning
↓
Machine Learning & Data Science for Beginners in Python
↓
Natural Language Processing in Python for Beginners
↓
Deep Learning for Beginners with Python
↓
Advanced Machine Learning and Deep Learning Projects

πŸš€ Production Engineer Path

Machine Learning & Data Science for Beginners in Python
↓
Deep Learning for Beginners with Python
↓
Data Visualization in Python Masterclass for Beginners
↓
Deploy ML Model in Production with FastAPI and Docker

πŸŽ“ Complete Mastery Path

Machine Learning & Data Science for Beginners in Python
↓
Deep Learning for Beginners with Python
↓
Natural Language Processing in Python for Beginners
↓
Master OpenAI Agent Builder - Low-Code AI Projects Workflow
↓
Master Langchain and Ollama - Chatbot, RAG and Agents
↓
Master LangGraph and LangChain with Ollama
↓
Agentic RAG with LangChain and LangGraph - Ollama
↓
MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
↓
Advanced Machine Learning and Deep Learning Projects
↓
Data Visualization in Python Masterclass for Beginners
↓
Deploy ML Model in Production with FastAPI and Docker
↓
Fine Tuning LLM with Hugging Face Transformers for NLP

πŸ† What Students Say

πŸ’¬ Student Success Stories

"The MCP course is absolutely game-changing! I went from zero knowledge to building production-ready AI applications in just a week."

"Best LangChain course on the internet. Practical, up-to-date, and the projects are industry-relevant."

"Finally understood how to deploy ML models properly. The FastAPI + Docker approach saved my company thousands."


πŸ“Š Course Statistics

πŸ“ˆ Metric 🎯 Achievement
Total Students 100,000+ Active Learners
Course Rating ⭐⭐⭐⭐⭐ (4.8/5.0)
Courses Available 11+ Comprehensive Programs
Hours of Content 100+ Hours of Learning
Projects Included 50+ Hands-on Projects
Technologies Covered 30+ Modern Frameworks

🌐 Connect with Me

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⭐ If this helped you, please give it a star! ⭐

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This repository contains implementations of Retrieval-Augmented Generation (RAG) in Jupyter notebooks. It includes examples of building chatbots with and without history, processing PDFs with RAG, and using DeepSeek models for local RAG and financial document analysis.

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