KGP Talkie
Structured Learning

AI Agent, RAG & Machine Learning Udemy Courses

Enroll in top-rated courses on AI agents, RAG, LangChain, MCP, and machine learning. Lifetime updates, structured Git repositories, Q&A support, and official completion certificates.

18
Courses
200,000+
Enrollments
4.8★
Instructor rating

Course index

All 18 courses listed in the sidebar. Pick one to jump straight to it.

01

AI Foundations

Start here if AI is new to you. Learn how modern AI works in plain words, which tools to use for free, and how decision models like Jev and Laya make fast, low-cost choices.

2 Structured CoursesSelf paced, on demand
Skills you will master
AI in Plain WordsUnderstand models, tokens, context windows and training without any coding or maths.
Free AI ToolsPick the right assistant, a free option for every paid tool, and free API keys.
Local AIRun models on your own computer with Ollama, LM Studio and Open WebUI.
Decision ModelsUse Jev and Laya to return typed choices and scores with calibrated confidence.
How AI Works: The Complete Beginners Guide to Modern AI
AI Foundations

How AI Works: The Complete Beginners Guide to Modern AI

ChatGPT, Claude, free AI tools, local AI and coding agents, explained in plain words inside the real tools. No coding and no maths.

  • AI in Plain Words: Models, training, tokens, context windows and temperature, explained simply
  • AI Assistants: ChatGPT, Claude, Gemini, Copilot, Perplexity and more, with free and paid plans compared
  • Free AI Toolkit: A free option for every paid tool, plus free API keys from Google AI Studio, OpenRouter and Groq
  • Local AI: Run models on your own computer with Ollama, LM Studio and Open WebUI, and know what your GPU, laptop or Mac can run
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Jev AI and Laya Fundamentals: Decision AI for Beginners
AI Foundations

Jev AI and Laya Fundamentals: Decision AI for Beginners

Build fast, low-cost AI decisions with Jev, Laya and System One models: Choice, Score, confidence and calibration.

  • Decision Models: What a System One model is and how Jev differs from a chat model like GPT or Claude
  • Question Types: Choice, Score and Noul, and when to use each one
  • Confidence and Calibration: Read probabilities and set safe thresholds from your own labelled data
  • Hosted and Local: Call Jev through the TypeSafe API and OpenRouter, and run Laya locally with Ollaya and Ollama
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02

LangChain & Ollama

Master local LLM integration and agent workflows. Build self-correcting RAG systems, chatbots, and stateful multi-agent architectures using LangChain, LangGraph, and Ollama.

4 Structured CoursesSelf paced, on demand100,000+ enrolled students
Skills you will master
Agentic RAGImplement corrective, self-improving RAG systems with local models like DeepSeek R1 and Llama.
LangGraph WorkflowsDesign stateful multi-agent loops and state machine graphs for complex tasks.
Local LLMs & OllamaRun open-source models locally without external API dependencies or costs.
Vector DatabasesIntegrate FAISS and ChromaDB vector stores for efficient semantic context retrieval.
Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents
LangChain & Ollama

Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents

Master Langchain v1, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide.

  • Setup & Integration: Professional Ollama and Langchain configuration
  • Custom Chatbots: Memory, history, and advanced features with Streamlit
  • Prompt Engineering: Templates, chains (Sequential, Parallel, Router)
  • Agent Development: Custom tools and step-by-step instruction execution
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Master LangGraph v1 and Ollama - Build Gen AI Agents
LangChain & Ollama

Master LangGraph v1 and Ollama - Build Gen AI Agents

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

  • Memory-Enabled Chatbots: Dynamic conversations with persistent memory
  • Database Integration: Direct MySQL query execution with LLMs
  • State Management: LangGraph workflows with advanced state machines
  • Private Data RAG: Custom embeddings and vector database integration
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MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
LangChain & Ollama

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

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

  • MCP Architecture: Client, server, and transport layers
  • Claude Desktop Integration: Direct MCP server connections
  • Real-World Applications: Data analysis servers for Excel, PowerPoint, SQLite
  • RAG Implementation: Vector databases with LangChain integration
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Agentic AI - Private Agentic RAG with LangGraph and Ollama
LangChain & Ollama

Agentic AI - Private Agentic RAG with LangGraph and Ollama

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

  • Agentic RAG: Intelligent, adaptive systems that act like smart assistants
  • Corrective RAG: Self-improving and error-correcting mechanisms
  • Document Processing: Doclings integration for efficient document loading
  • Production Ready: Streamlit apps and AWS EC2 deployment
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03

AI Agents & Advanced

Dive into production-ready agent engineering, multi-agent orchestrations, MCP servers, LLM fine-tuning with Hugging Face for custom NLP tasks, and GenAI system design interviews.

7 Structured CoursesSelf paced, on demand80,000+ enrolled students
Skills you will master
Multi-Agent SystemsDesign supervisor agents, specialist routing, and coordination structures using LangGraph.
Model Fine-TuningAdapt open-source models to proprietary datasets using Hugging Face and PyTorch.
Model Context Protocol (MCP)Connect agents to files, SQLite databases, financial tools, and local utilities.
Production API & UIsBuild streaming APIs with FastAPI and responsive user interfaces with Chainlit and Streamlit.
Master OpenAI Agent Builder - Deploy Chatbot to Your Website
AI Agents & Advanced

Master OpenAI Agent Builder - Deploy Chatbot to Your Website

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

  • Visual AI Development: Build AI agents without complex coding using OpenAI Agent Builder
  • Real-World Integration: Connect AI workflows with MySQL, AWS, and MCP connectors
  • Production Deployment: Deploy AI agents with ChatKit and Guardrails for safety
  • Complete Projects: Weather Agent, RAG Document Q&A Chatbot, E-Commerce AI Assistant
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Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3
AI Agents & Advanced

Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3

Build real AI agents using LangChain and Google Gemini, deploy with FastAPI and AWS EC2.

  • Agent Architecture: ReAct reasoning, tool calling, and structured decision making
  • Memory Systems: Short-term and long-term memory using databases and embeddings
  • Safety & Guardrails: Human-in-the-loop, middleware controls, and sandboxed code execution
  • Production APIs: FastAPI REST endpoints with validation, CORS, and SSE streaming
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Fine Tuning LLM with Hugging Face Transformers for NLP
AI Agents & Advanced

Fine Tuning LLM with Hugging Face Transformers for NLP

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

  • Transformer Deep Dive: Architecture fundamentals and mathematical foundations
  • Custom Dataset Preparation: Data preprocessing and formatting techniques
  • Fine-tuning Mastery: Advanced optimization and training strategies
  • Model Optimization: Performance tuning and evaluation methodologies
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Deep Agent - Multi Agent RAG with Gemini and Langchain
AI Agents & Advanced

Deep Agent - Multi Agent RAG with Gemini and Langchain

Build real-world AI agents and deep research systems using Google Gemini, LangChain v1, MCP, and modern RAG techniques.

  • Agent Foundations: ReAct patterns, tool calling, memory, and state management
  • Gemini + LangChain Bootcamp: Streaming, multimodal inputs, function calling, and context caching
  • MCP Finance Agent: Connect Yahoo Finance MCP server as LangChain tools for stock research
  • Multimodal Deep RAG: Extract and process financial PDFs, tables, and images with Docling
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Advanced RAG: Build & Deploy Production GenAI Apps
AI Agents & Advanced

Advanced RAG: Build & Deploy Production GenAI Apps

Build RAGWire, a production-grade RAG toolkit with LangChain, Qdrant, and LangGraph, from hybrid search to multi-cloud deployment.

  • Hybrid RAG Pipeline: BM25 sparse + dense retrieval with Reciprocal Rank Fusion (RRF)
  • Multi-LLM Support: OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings
  • Agentic RAG: Self-correcting agents that grade retrieval quality and rewrite queries
  • Multi-Agent Systems: Supervisor agents with CrewAI, Microsoft AutoGen, and LangGraph routing
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Blender MCP & Claude AI: Hunyuan 3D, Characters & AI Agents
AI Agents & Advanced

Blender MCP & Claude AI: Hunyuan 3D, Characters & AI Agents

Connect Claude AI to Blender via MCP, automate 3D scenes, AI character pipelines and product animations with Python.

  • Blender MCP Setup: Build the Blender MCP server and connect Claude AI to Blender
  • Scene Automation: 3D scenes, lighting, camera control and product animations from prompts
  • Agentic Workflows: The iterative AI workflow and Claude Skills for complex scenes
  • Image to 3D: Generate 3D characters locally with Hunyuan 3D, Gradio and Claude AI
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Mastering Gen AI System Design Interviews
AI Agents & Advanced

Mastering Gen AI System Design Interviews

Design LLM apps the way interviewers expect: model internals, RAG, agents, GPUs, cost and safety, with 4 full mock designs.

  • Answer Framework: One six-step method for any GenAI design question, from requirements to evaluation
  • Model Internals: Prefill and decode, the KV cache, FlashAttention, PagedAttention, batching and speculative decoding
  • RAG and Agents: Chunking, hybrid search, reranking, tool calling, MCP and guardrails on actions
  • Production and GPU Sizing: Cost, latency, safety and monitoring, plus sizing self-hosted GPUs for N users
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04

Machine Learning and Data Science

Establish a bulletproof foundation in mathematical and statistical analysis, regression modeling, exploratory data analysis (EDA), and core machine learning models.

4 Structured CoursesSelf paced, on demand120,000+ enrolled students
Skills you will master
Data PreprocessingMaster NumPy and Pandas to clean, structure, and manipulate raw dataset files.
ML AlgorithmsImplement supervised and unsupervised models including linear regression and decision trees.
Deep Learning BasicsBuild Artificial Neural Networks (ANN) and sequence models (LSTM) using TensorFlow.
Feature EngineeringExtract numeric values from messy text, handle missing data, and normalize input features.
Machine Learning & Data Science for Beginners in Python
Machine Learning and Data Science

Machine Learning & Data Science for Beginners in Python

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

  • Python for Data Science: From basics to advanced data manipulation
  • Data Analysis Mastery: Pandas, NumPy, and exploratory data analysis
  • Machine Learning: Supervised and unsupervised learning algorithms
  • Deep Learning Introduction: Neural networks with Keras and TensorFlow
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Python for Linear Regression in Machine Learning
Machine Learning and Data Science

Python for Linear Regression in Machine Learning

Master statistical foundations and practical implementation of regression analysis.

  • Regression Theory: Mathematical foundations and statistical principles
  • Hypothesis Testing: Statistical validation and significance testing
  • Feature Engineering: Variable selection and transformation techniques
  • Model Evaluation: R-squared, RMSE, and comprehensive diagnostics
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Deep Learning for Beginners with Python
Machine Learning and Data Science

Deep Learning for Beginners with Python

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

  • Artificial Neural Networks (ANN): Build from mathematical foundations
  • Convolutional Neural Networks (CNN): Image processing and computer vision
  • Recurrent Neural Networks (RNN): Sequential data and time series analysis
  • LSTM Networks: Advanced sequence modeling and memory networks
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Natural Language Processing (NLP) Mastery in Python
Machine Learning and Data Science

Natural Language Processing (NLP) Mastery in Python

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

  • Text Processing: Spacy and NLTK for production-ready NLP
  • Sentiment Analysis: Emotion detection and opinion mining
  • Named Entity Recognition: Extract people, places, organizations
  • Text Classification: Document categorization and content analysis
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05

Production and Deployment

Bridge the gap between experimental notebooks and production code. Learn how to wrap models in FastAPI microservices, containerize with Docker, and deploy to AWS at scale.

1 Structured CourseSelf paced, on demand50,000+ enrolled students
Skills you will master
FastAPI MicroservicesExpose trained model predictions through high-performance REST APIs.
Docker ContainersPackage applications into lightweight, self-contained units for reliable cross-platform execution.
AWS Cloud HostingDeploy secure, production-ready instances on EC2, Lambda, and API Gateway.
DevOps & MonitoringConfigure NGINX, uWSGI, uvicorn, and implement automated prediction logging and health checks.
Deploy ML Model in Production with FastAPI and Docker
Production and Deployment

Deploy ML Model in Production with FastAPI and Docker

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

  • FastAPI Development: High-performance API creation for ML models
  • Docker Containerization: Scalable and portable deployment solutions
  • Cloud Deployment: AWS, GCP, and Azure deployment strategies
  • Security & Monitoring: Authentication, logging, and performance monitoring
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