Full-Stack Ai With Python: Llms, Rag, Agents & Langgraph

Hands-on guide to modern AI: Tokenization, Agents, RAG, Vector DBs, and deploying scalable AI apps. Complete AI course

What you'll learn

Write Python programs from scratch, using Git for version control and Docker for deployment.

Use Pydantic to handle structured data and validation in Python applications.

Understand how Large Language Models (LLMs) work: tokenization, embeddings, attention, and transformers.

Call and integrate APIs from OpenAI and Gemini with Python.

Design effective prompts: zero-shot, one-shot, few-shot, chain-of-thought, persona-based, and structured prompting.

Run and deploy models locally using Ollama, Hugging Face, and Docker.

Implement Retrieval-Augmented Generation (RAG) pipelines with LangChain and vector databases.

Use LangGraph to design stateful AI systems with nodes, edges, and checkpointing.

Understand Model Context Protocol (MCP) and build MCP servers with Python.

Requirements

No prior AI knowledge is required — we start from the basics.

A computer (Windows, macOS, or Linux) with internet access.

Basic programming knowledge is helpful but not mandatory (the course covers Python from scratch).

Description

Welcome to the Complete AI & LLM Engineering Bootcamp – your one-stop course to learn Python, Git, Docker, Pydantic, LLMs, Agents, RAG, LangChain, LangGraph, and Multi-Modal AI from the ground up.This is not just another theory course. By the end, you will be able to code, deploy, and scale real-world AI applications that use the same techniques powering ChatGPT, Gemini, and Claude. What You’ll LearnFoundationsPython programming from scratch — syntax, data types, OOP, and advanced features.Git & GitHub essentials — branching, merging, collaboration, and professional workflows.Docker — containerization, images, volumes, and deploying applications like a pro.Pydantic — type-safe, structured data handling for modern Python apps.AI FundamentalsWhat are LLMs and how GPT works under the hood.Tokenization, embeddings, attention, and transformers explained simply.Understanding multi-head attention, positional encodings, and the "Attention is All You Need" paper.Prompt EngineeringMaster prompting strategies: zero-shot, one-shot, few-shot, chain-of-thought, persona-based prompts.Using Alpaca, ChatML, and LLaMA-2 formats.Designing prompts for structured outputs with Pydantic.Running & Using LLMsSetting up OpenAI & Gemini APIs with Python.Running models locally with Ollama + Docker.Using Hugging Face models and INSTRUCT-tuned models.Connecting LLMs to FastAPI endpoints.Agents & RAG SystemsBuild your first AI Agent from scratch.CLI-based coding agents with Claude.The complete RAG pipeline — indexing, retrieval, and answering.LangChain: document loaders, splitters, retrievers, and vector stores.Advanced RAG with Redis/Valkey Queues for async processing.Scaling RAG with workers and FastAPI.LangGraph & MemoryIntroduction to LangGraph — state, nodes, edges, and graph-based AI.Adding checkpointing with MongoDB.Memory systems: short-term, long-term, episodic, semantic memory.Implementing memory layers with Mem0 and Vector DB.Graph memory with Neo4j and Cypher queries.Conversational & Multi-Modal AIBuild voice-based conversational agents.Integrate speech-to-text (STT) and text-to-speech (TTS).Code your own AI voice assistant for coding (Cursor IDE clone).Multi-modal LLMs: process images and text together.Model Context Protocol (MCP)What is MCP and why it matters for AI apps.MCP transports: STDIO and SSE.Coding an MCP server with Python. Real-World Projects You’ll BuildTokenizer from scratch.Local Ollama + FastAPI AI app.Python CLI-based coding assistant.Document RAG pipeline with LangChain & Vector DB.Queue-based scalable RAG system with Redis & FastAPI.AI conversational voice agent (STT + GPT + TTS).Graph memory agent with Neo4j.MCP-powered AI server. Who Is This Course For?Beginners who want a complete start-to-finish course on Python + AI.Developers who want to build real-world AI apps using LLMs, RAG, and LangChain.Data Engineers/Backend Developers looking to integrate AI into existing stacks.Students & Professionals aiming to upskill in modern AI engineering. Why Take This Course?This course combines theory, coding, and deployment in one place. You’ll start from the basics of Python and Git, and by the end, you’ll be coding cutting-edge AI applications with LangChain, LangGraph, Ollama, Hugging Face, and more.Unlike other courses, this one doesn’t stop at “calling APIs.” You will go deeper into system design, queues, scaling, memory, and graph-powered AI agents — everything you need to stand out as an AI Engineer.By the end of this course, you won’t just understand AI—you’ll be able to build it.

Overview

Section 1: Introduction

Lecture 1 Installation of Tools (VSCode and Python)

Lecture 2 VS Code Setup (Extensions and Themes)

Lecture 3 Get your code files here

Section 2: Introduction to Coding world with python

Lecture 4 Meet your Instructor - Hitesh

Lecture 5 What is Programming..?

Lecture 6 Convert that into Python Code

Lecture 7 A Real World Python Code Intro

Lecture 8 Why to use Python

Lecture 9 Writing first Python code on MAC

Lecture 10 Writing first Python code on WINDOWS

Lecture 11 Get everything in Virtual Environment

Lecture 12 Organize Python Code like a Pro

Lecture 13 PEP8 and Zen of python

Section 3: Data Types in Python

Lecture 14 Objects - Mutable and Immutable in Python

Lecture 15 Numbers, Booleans and Operators in Depth in Python

Lecture 16 String - Index, Slice and Encoding

Lecture 17 Tuples and Membership Testing

Lecture 18 Basics of List in Python

Lecture 19 Operator overloading and bytearray in python

Lecture 20 Set and frozenset in python

Lecture 21 Dictionary in Python

Lecture 22 Touch on Advance Data types like Collections

Section 4: Conditionals in python

Lecture 23 Kettle Boiling Story Project

Lecture 24 Building a Snack System

Lecture 25 Building a Chai Price Calculator

Lecture 26 Building Smart Thermostat System

Lecture 27 Delivery Fees Waiver System

Lecture 28 Build a train seat information system

Section 5: Loops in python

Lecture 29 Introduction to Loops

Lecture 30 Tea Token Dispenser

Lecture 31 Batch Chai Preparation

Lecture 32 Looping through list - Orders Name

Lecture 33 Why to use Enumerate

Lecture 34 Zip Can Combine Lists

Lecture 35 Introducing While Loop in Python

Lecture 36 Break, Continue and Loop Fallback

Lecture 37 Walrus Operator is Interesting in Python

Lecture 38 Dictionary in place of Match Case

Section 6: Functions in python

Lecture 39 Functions - Reducing Duplication and Splitting Complex Tasks

Lecture 40 Functions - 3 More Features

Lecture 41 Scope and Named Space in Functions

Lecture 42 Non local vs Global scopes

Lecture 43 Handling Arguments in Function in Python

Lecture 44 Handle Multiple Return in Python

Lecture 45 Lambdas, Pure vs Impure functions

Lecture 46 Documenting your Functions and Built-in Functions

Lecture 47 Python Imports, Modules and Init File

Section 7: Comprehensions in python

Lecture 48 What are Comprehensions in Python?

Lecture 49 List Comprehensions in Python

Lecture 50 Set Comprehensions in Python

Lecture 51 Dictionary Comprehensions in Python

Lecture 52 Generator Comprehensions for Memory Optimization

Section 8: Generators and Decorators in python

Lecture 53 Generators with Yield and Next Methods

Lecture 54 Infinite Generators in Python

Lecture 55 Send Value to Generators

Lecture 56 Yield From and Close the Generators

Lecture 57 Decorators in Python

Lecture 58 Build a Logger with Decorator

Lecture 59 Build an Authorization Decorator

Section 9: Object oriented programming in python

Lecture 60 Building your 1st Class and Object in Python

Lecture 61 Class and Object Namespace

Lecture 62 Attribute Shadowing in Python

Lecture 63 Self argument in python

Lecture 64 Constructors and Init in Python Classes

Lecture 65 Inheritance and Composition in Python Classes

Lecture 66 3 Ways to Access Base Class

Lecture 67 Method Resolution Order - MRO

Lecture 68 Static Methods in Python

Lecture 69 Classmethod vs Staticmethod

Lecture 70 Property decorator - Getter and Setter

Section 10: File and exception handling in python

Lecture 71 What is Error handling

Lecture 72 Try except else and finally

Lecture 73 Catching multiple exceptions

Lecture 74 Raise your own errors

Lecture 75 Creating custom exceptions

Lecture 76 Mini project with exception learning

Lecture 77 File handling with try except and with

Section 11: MultiThreading, Multiprocessing, GIL in python

Lecture 78 Code files for Thread and concurrency section

Lecture 79 What is Concurrency and Parallelism?

Lecture 80 What is Global Interpreter Lock - GIL

Lecture 81 Threads and lock in depth

Lecture 82 Multi Process with Queue and Value

Section 12: Asyncio in python

Lecture 83 Code files for asyncio section

Lecture 84 Asyncio, Event loop, coroutines and await in python

Lecture 85 Mixing threads with asyncio in python

Lecture 86 Asyncio and MultiProcess in python

Lecture 87 Understanding Daemon vs Non-Daemon Threads

Lecture 88 Debugging and Profiling - Race condition and Deadlock in python

Section 13: All that you should know about pydantic

Lecture 89 Why pydantic is important

Lecture 90 The foundation of pydantic

Lecture 91 Pydantic Default conversions

Lecture 92 Mixing pydantic and typing in python

Lecture 93 Adding validations with Field

Lecture 94 Field and model validators in python

Lecture 95 Computed property in pydantic

Lecture 96 Advance Validation in pydantic

Lecture 97 Nested models in pydantic

Lecture 98 Self referencing models in pydantic

Lecture 99 Advance nested model patterns

Lecture 100 Best practice for pydantic model design

Lecture 101 Model dump and model dump json in serialization of pydantic

Section 14: Core Foundations of Generative AI

Lecture 102 Understanding Large Language Models (LLMs)

Lecture 103 Deep Dive into the GPT Architecture

Lecture 104 How LLMS Work under the Hood?

Lecture 105 Fundamentals of Tokenization in NLP

Lecture 106 Implementing a Custom Tokenizer in Python

Lecture 107 The Transformer Breakthrough: Google’s Paper on Attention

Lecture 108 Deep Diving into Vector Embeddings

Lecture 109 Role of Positional Encodings in Transformers

Lecture 110 Understanding Multi-Head Attention for Rich Context

Section 15: API Setup & Integration

Lecture 111 Configuring Your OpenAI Account

Lecture 112 Invoking OpenAI APIs with Python

Lecture 113 Creating and Setting Up Google Gemini Account

Lecture 114 Using Google Gemini with OpenAI-Compatible APIs

Section 16: Advanced Prompt Engineering Techniques

Lecture 115 Prompt Fundamentals: Encoding Instructions for LLMs

Lecture 116 Prompting Types: Zero-Shot, Few-Shot, One-Shot

Lecture 117 One-Shot Prompting for Deterministic Inference

Lecture 118 Few-Shot Prompting for Contextual Generalization

Lecture 119 Structured Outputs with Few-Shot Prompting

Lecture 120 Chain-of-Thought (CoT) for Reasoning

Lecture 121 Auto-CoT: Automated Reasoning Prompt Generation

Lecture 122 Persona-Based Prompting

Section 17: Prompt Serialization & Instruction Formats

Lecture 123 Introduction to Prompt Serialization Styles

Lecture 124 Alpaca Prompt Template for Instruction Tuning

Lecture 125 ChatML Schema: OpenAI’s Structured Prompt Format

Lecture 126 INST Format: LLaMA-2 Instruction Specification

Section 18: Local LLM Deployment & API Integration

Lecture 127 Ollama Overview: Local LLM Runtime Engine

Lecture 128 Dockerized Environment Setup for LLMs

Lecture 129 Running Ollama Models with Docker Runner

Lecture 130 Configuring OpenWebUI with Ollama Backend

Lecture 131 FastAPI Environment Setup & Dependencies

Lecture 132 Integrating Ollama with FastAPI & Python APIs

Section 19: Running LLMs via Hugging Face Hub

Lecture 133 Hugging Face Model Deployment – Section Intro

Lecture 134 Configuring and Securing Hugging Face Account

Lecture 135 Accessing Instruct-Tuned Models (Google Gemma)

Lecture 136 Installing and Using Hugging Face CLI Tools

Lecture 137 Model Downloading & Execution from HF Hub

Section 20: Building AI Agents and Agentic Workflows

Lecture 138 Agentic AI Fundamentals – Section Intro

Lecture 139 What Exactly Are AI Agents? (Core Concepts)

Lecture 140 Coding Your First AI Agent

Lecture 141 Enforcing Structured Outputs with Pydantic

Lecture 142 Building a CLI Coding Agent (Claude Code) from Scratch

Section 21: Retrieval-Augmented Generation (RAG) Architectures and LangChain

Lecture 143 Intro to RAG & LangChain – Section Overview

Lecture 144 Defining the Core Problem in RAG Systems

Lecture 145 Naive Retrieval-Based Solution Approach

Lecture 146 RAG Pipeline – Indexing Workflow Explained

Lecture 147 RAG Pipeline – Retrieval Mechanism in Depth

Lecture 148 Local Vector DB Setup with Docker Compose

Lecture 149 LangChain Installation & Setup

Lecture 150 LangChain Document Loaders for PDF

Lecture 151 LangChain Document Chunking & Splitting

Lecture 152 LangChain Vector Store as Retrievers

Lecture 153 LangChain-Powered RAG Retrieval Execution

Section 22: Scalable RAG with Async Queues & Distributed Workers

Lecture 154 Sync vs Async in RAG Architectures

Lecture 155 Introduction to Queues System Design for Async Setup

Lecture 156 Setting up Redis and Valkey with Docker

Lecture 157 Python RQ Setup Distributed Queues

Lecture 158 Worker Orchestration with Python RQ

Lecture 159 FastAPI Endpoints setup for chat Queue

Lecture 160 Asynchronous Message Enqueueing with FastAPI

Lecture 161 FastAPI Polling & Dequeuing Messages from Async Queues

Lecture 162 Running & Scaling Worker Nodes for Background Processing

Section 23: Multi Modal Agents

Lecture 163 What is Multi Modal Agent?

Lecture 164 Sending Multimedia to LLM (Images)

Section 24: Building Agentic Workflows with LangGraph

Lecture 165 Section Intro - Why LangGraph is a Game-Changer for AI Agents

Lecture 166 Deep Dive into LangGraph – Core Concepts, Nodes and Edges

Lecture 167 Setting Up LangGraph – Installation and Environment Configuration

Lecture 168 Defining State in LangGraph for AI Agent Context

Lecture 169 Defining Nodes and Functions in LangGraph

Lecture 170 Connecting Nodes with Edges – Designing Complex AI Graph

Lecture 171 Testing and Debugging Your LangGraph AI Workflow

Lecture 172 Integrating AI LLMs into LangGraph

Lecture 173 Conditional Edges & Smart Routing

Section 25: Checkpointing Workflows in LangGraph with MongoDB

Lecture 174 What is Checkpointing? Enabling Persistence in AI Agent Workflows

Lecture 175 Setting Up MongoDB with Docker for LangGraph Checkpoint Storage

Lecture 176 Implementing MongoDB Checkpointer in LangGraph Workflow Graphs

Section 26: The Memory Layer – Building Short, Long, and Semantic Memory in AI Agents

Lecture 177 Section Intro - The Memory Layer in AI Agents

Lecture 178 What is Memory in AI and Agents

Lecture 179 Different Types of Memory Architectures in AI and Agent

Lecture 180 Short-Term Memory – Handling Context Windows

Lecture 181 Long-Term Memory – Persistent Knowledge

Lecture 182 Factual Memory for AI Agents

Lecture 183 Episodic Memory in AI Workflows

Lecture 184 Semantic Memory for General Knowledge

Lecture 185 Mem0 Setup with Python for AI Memory Layer

Lecture 186 Mem0 Configuration with Python for Agents

Lecture 187 Vector Database Setup with Docker for Memory

Lecture 188 Using Vector Databases for AI Agent Memory

Section 27: Graph Memory and Knowledge Graphs In AI Agents

Lecture 189 Section Intro to the Graph Memory

Lecture 190 What is a Graph in AI and Data Systems

Lecture 191 Why Graph Memory is Needed in AI Agents

Lecture 192 Introduction to Graph Databases Neo4j and Kuzu

Lecture 193 Setting Up Neo4j Cloud Instance for Graph Memory

Lecture 194 Basics of Cypher Query for Graph Databases

Lecture 195 Adding Graph Database Support for Memory Agent

Lecture 196 Testing Graph Memory Implementation in Agents

Section 28: Conversational Agentic AI with Voice Agents and Chained Patterns

Lecture 197 Section Intro to Conversational Agentic AI

Lecture 198 Understanding Conversational AI for Agents

Lecture 199 The S2S and Chained Voice Agents

Lecture 200 Speech To Speech Voice Agents

Lecture 201 Understanding the Chained Pattern for Voice Agents

Lecture 202 Setting Up STT for Chained Conversational Agent

Lecture 203 Setting Up OpenAI GPT Completions for Chained Agent

Lecture 204 Setting Up TTS for Conversational AI Agents

Lecture 205 Building a Voice Based AI Cursor IDE Clone

Section 29: Model Context Protocol - MCP

Lecture 206 Section Intro to Model Context Protocol

Lecture 207 Understanding What Model Context Protocol (MCP) Is

Lecture 208 Exploring the Architecture of MCP

Section 30: Git - Additional Learning

Lecture 209 Introduction to GIT series

Lecture 210 Git init and hidden folder

Lecture 211 Git commits and logs

Lecture 212 Git internal working and configs

Lecture 213 Git merge and git conflicts

Lecture 214 Git Diff and stashing

Lecture 215 Git rebase is not that scary

Lecture 216 Insight of pushing code to github

Lecture 217 How to make Pull Request and Open Source contribution

Section 31: Mastering Docker for Developers – From Basics to CLI and Dockerfile

Lecture 218 Introduction to Docker and the Rise of Containerization in DevOps

Lecture 219 Real-World Problem That Docker Solves in Modern Development

Lecture 220 Understanding the Difference Between Docker and Virtual Machines

Lecture 221 How to Install Docker on Your System for Local Development

Lecture 222 Docker Containers vs Docker Images: What's the Difference?

Lecture 223 Introduction to Docker CLI and Commonly Used Commands

Lecture 224 Running Docker Containers Using the CLI with Practical Examples

Lecture 225 Working with Docker Images Through Command-Line Interface (CLI)

Lecture 226 Exploring Docker Container Commands for Management and Debugging

Lecture 227 Creating and Using a Dockerfile to Containerize Node.js Apps

Lecture 228 Best Practices to Optimize Docker Images for Speed and Performance

Lecture 229 Understanding and Implementing Port Mapping in Docker Containers

Lecture 230 Auto Port Mapping in Docker: Dynamic Exposure of Container Ports

Lecture 231 Publishing Docker Images to Docker Hub or Private Registries

Lecture 232 Building Optimized Multi-Stage Docker Images for Production Use

Lecture 233 Security Best Practices for Running Docker Containers Safely

Lecture 234 Understanding Docker Bridge Networking for Container Communication

Lecture 235 Creating and Using Custom Docker Bridges for Network Isolation

Lecture 236 Docker Other Modes of Networking

Lecture 237 Attaching Host Machine Volumes to Docker Containers for Data Sharing

Lecture 238 Creating and Managing Custom Named Volumes in Docker for Persistence

Lecture 239 Introduction to Docker Compose

Lecture 240 Networking in Docker Compose

Lecture 241 Volume in Docker Compose

Lecture 242 Custom Docker builds

Lecture 243 Introduction to Docker Orchestration and Why It’s Crucial for Production

Lecture 244 Creating and Configuring a New AWS Account for ECS Deployment

Lecture 245 Setting Up Amazon Elastic Container Registry (ECR) to Push Docker Images

Lecture 246 Launching and Configuring ECS Clusters to Run Docker Containers

Lecture 247 Defining ECS Tasks and Creating Task Definitions for Container Execution

Lecture 248 Deploying ECS Services with Load Balancer for High Availability

Lecture 249 Cleaning Up AWS ECS and ECR Resources to Avoid Unnecessary Billing

Lecture 250 Debugging and Fixing ECS Health Check Failures During Container Deployment

Section 32: Farewell

Lecture 251 farewell

Beginners who want a step-by-step path into AI, Python, and modern development tools.,Developers who want to learn how to integrate LLMs, RAG, and agents into real-world applications.,Data engineers and backend developers looking to upgrade their skills with AI-powered systems.,Students and professionals who want to stand out in the job market with cutting-edge AI engineering knowledge.