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Complete Generative AI, Agentic AI & RAG Bootcamp

Master Generative AI, LangChain, LangGraph, Hugging Face, RAG, Multi-Agent Systems and LangSmith with Real Projects

Role Play

New

Created byMOHD SAQIB

Last updated 8/2026

English

What you'll learn

Build advanced Generative AI applications using LangChain and Hugging Face models.
Understand the architecture, design patterns, and best practices for creating robust GenAI systems.
Deploy Generative AI applications to cloud platforms and on-premise environments with scalability and reliability.
Develop Retrieval-Augmented Generation (RAG) pipelines to improve accuracy and information retrieval.
Integrate Hugging Face pre-trained models into LangChain applications for powerful NLP workflows.
Customize and fine-tune models for real-world use cases such as chatbots, content generation, and data augmentation.
Learn the core concepts of Agentic AI and design intelligent autonomous agents for practical tasks.
Build agents using LangGraph, including state management, memory, workflows, and event-driven behavior.
Create multi-agent systems that collaborate, communicate, and solve complex problems together.
Implement advanced RAG systems using hybrid search, multimodal retrieval, and persistent memory
Use LangSmith to track, debug, and optimize RAG and agent workflows.
Work on hands-on projects such as autonomous research agents, task automation systems, knowledge assistants, and multimodal AI applications.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

33 sections • 164 lectures • 30h 38m total length

Welcome to the course1:39
Welcome to the course! In this lecture, you'll get an overview of what this course offers and how it will help you build practical Generative AI applications. By the end, you'll be ready to begin your AI learning journey with confidence.
What you will build in this course1:03
Explore the exciting AI applications, tools, and projects you'll build throughout this course. This lecture provides a preview of your learning journey and the practical skills you'll develop.
How this course is structured1:04
Understand how the course is organized and how each section builds upon the previous one. You'll learn the recommended learning path to get the maximum benefit from the course.
Tools, software, and accounts needed0:52
Get a quick overview of the tools, software, and technologies that will be used throughout the course. Detailed installation and setup will be covered in the upcoming sections.
How to follow the course effectively0:51
Learn the best practices for following this course effectively, including coding along, practicing regularly, and using the available learning resources to strengthen your understanding.
Course resources and project files0:59
Discover the learning resources available throughout the course, including source code, project files, premium notes, assignments, quizzes, and GitHub resources that will support your learning.

Install Python and VS Code5:56
Understand the software and hardware requirements needed for this course. This lecture ensures your system is ready before beginning the practical implementation
Install Anaconda and Conda basics6:29
Install Python and verify your development environment correctly. By the end of this lecture, you'll have Python ready for AI development.
Create virtual environments with Conda8:20
Learn how to install Visual Studio Code and configure it for Python development. You'll also explore useful extensions that improve your coding experience.
Create virtual environments with UV2:42
Set up virtual environments to isolate project dependencies and keep your AI projects organized. This is an essential practice for professional Python development.
Jupyter notebooks vs scripts5:23
Install Git and configure GitHub for version control. You'll understand how to manage your code efficiently throughout the course.
Installing libraries with pip and UV7:31
Complete your local development environment setup and verify that everything is working correctly before moving on to coding exercises.
Environment & Environment Management
Verify Python Package Installation and Environment Information
AI Development Environment Setup
Troubleshooting Python Virtual Environments

Python Core Concepts in One Video( part 1)34:54
Learn the core Python concepts in a single focused lesson, part 1. This lecture builds the foundation needed for the rest of the programming sections.
Python Core Concepts in One Video( part 2)34:54
Continue the core Python concepts in part 2 and deepen your understanding of the language. This lecture completes the essential Python foundation before moving to functions.
Python Core Fundamentals
Build an AI Prompt Data Processor

Function basics1:30
Learn the basics of Python functions and how they help you organize reusable code. This lecture introduces one of the most important building blocks in Python programming.
Default, Keyword & Variable Arguments, Lambda Functions, Map & Filter24:39
Explore default, keyword, and variable arguments, along with lambda functions, map, and filter. This lecture shows how to write more flexible and expressive Python code.
Importing modules and packages3:46
Learn how to import modules and packages correctly in Python projects. This lecture helps students structure code better and reuse existing functionality.
Writing reusable python code9:37
Write reusable Python code with cleaner structure and better modular design. This lecture helps students move from small examples to more maintainable programs.

File operations4:09
Understand file operations and how programs interact with files on disk. This lecture introduces the basics of reading from and writing to files.
Reading and writing text files1:44
Learn how to read and write text files in Python. This lecture covers practical file-handling patterns used in many real projects.
Reading structured data files10:58
Read structured data files and work with organized content in Python. This lecture prepares students for real-world data processing workflows.
Custom exception handling6:12
Handle custom exceptions and improve error handling in your programs. This lecture helps students write safer and more reliable code

Classes and objects7:28
Learn what classes and objects are and how object-oriented programming starts in Python. This lecture introduces the core OOP mindset.
constructors and attributes16:08
Understand constructors and attributes and how objects are initialized. This lecture helps students build better object-based programs.
Inheritance14:35
Learn inheritance and how child classes extend parent class behavior. This lecture introduces code reuse through class relationships.
Polymorphism9:08
Explore polymorphism and how different objects can respond differently to the same method call. This lecture strengthens understanding of flexible class design.
Encapsulation10:58
Learn encapsulation and how to protect and organize internal object data. This lecture covers one of the key principles of OOP design
Abstraction7:54
Understand abstraction and how to hide complexity behind simple interfaces. This lecture helps students design cleaner and more user-friendly class structures.
Magic methods11:09
Learn about magic methods and how Python uses them to customize object behavior. This lecture introduces special methods that make classes more powerful.
Operator overloading8:55
Explore operator overloading and see how Python operators can be customized for your own classes. This lecture shows how to make objects behave more naturally.
Build a Production-Style AI Assistant Using OOP8:35
Build a production-style AI assistant using OOP and combine object-oriented thinking with AI application design. This lecture applies the earlier OOP concepts in a practical project

Iterators10:52
Learn about iterators and how Python handles values one step at a time. This lecture introduces the foundation for efficient looping and data traversal.
Generators8:31
Explore generators and how they help produce values lazily. This lecture shows how to write memory-efficient Python code.
Yield and lazy evaluation5:51
Understand yield and lazy evaluation and why they matter in generators. This lecture builds a deeper understanding of how Python processes data efficiently.
Closures7:51
Learn closures and how inner functions can remember variables from their outer scope. This lecture introduces a powerful functional-programming concept
Decorators11:00
Explore decorators and how they let you extend function behavior cleanly. This lecture teaches a popular Python pattern used in real applications.
Practical utility patterns7:23
Practice practical utility patterns and learn how these Python concepts come together in useful code. This lecture helps students apply iterators, generators, closures, and decorators in realistic ways.

Complete Python Logging Guide: From Basics to Production25:35
Learn Python logging from basics to production and understand why logs are essential in real systems. This lecture covers how to track app behavior and debug effectively.
Pydantic Data Validation: Fields, Constraints & Custom Validators21:40
Learn Pydantic data validation with fields, constraints, and custom validators. This lecture shows how to make data handling more reliable and structured.
Pydantic Data Models: Reusable Models, Serialization & AI Examples7:52
Build reusable Pydantic data models and explore serialization with AI examples. This lecture helps students design cleaner and more production-ready data models

NumPy Fundamentals for AI and Data Science23:18
Learn the fundamentals of NumPy and understand how it supports numerical computing in AI and data science. This lecture builds the foundation for array-based operations used throughout the course.
Data Analysis with Pandas28:33
Explore data analysis with Pandas and learn how to work with tabular data efficiently. This lecture introduces the core workflow for inspecting and analyzing structured datasets.
Data manipulation with Pandas20:43
Learn practical data manipulation with Pandas and understand how to clean, filter, and transform data. This lecture helps you prepare datasets for machine learning and AI tasks.
Data Visualization with Matplotlib34:08
Discover how to visualize data using Matplotlib and present insights clearly. This lecture shows how charts and plots help communicate patterns in data.

Getting started with Streamlit & Build your first app8:46
Get started with Streamlit and build your first AI app interface. This lecture introduces a simple and practical way to turn Python code into interactive web apps.
Widgets and interaction8:52
Learn about Streamlit widgets and interaction to make your app more dynamic. This lecture shows how users can input values and interact with your application.
Displaying outputs11:08
Explore how to display outputs clearly in Streamlit applications. This lecture helps you present results in a clean and user-friendly way.
Displaying with ML/AI apps7:27
Learn how to display machine learning and AI outputs inside Streamlit apps. This lecture connects your AI logic with a practical visual interface.
Deploying simple demo apps10:04
Understand how to deploy simple demo apps so others can use your Streamlit projects. This lecture gives you a basic introduction to sharing your work online.

Requirements

No prior knowledge of RAG is required — everything will be taught from scratch.
Familiarity with Python programming language, including basic syntax, data structures, and functions.
Ability to navigate and execute commands in a command line interface (CLI) or terminal.

Description

Welcome to the Complete Generative AI, Agentic AI & RAG Bootcamp, a comprehensive hands-on course designed to take you from beginner to advanced AI Engineer.

In this course, you will master the complete modern AI stack including Large Language Models (LLMs), LangChain, Hugging Face, Agentic AI, LangGraph, Retrieval-Augmented Generation (RAG), LangSmith, Vector Databases, and Multi-Agent Systems.

You will begin with the fundamentals of Generative AI and understand how LLMs work. Then, you will learn how to build powerful AI applications using LangChain and Hugging Face models. You'll discover prompt engineering techniques, chains, memory, tools, document loaders, embeddings, and production-ready workflows.

Next, you will dive deep into Agentic AI and learn to build autonomous agents with LangGraph. You will understand state management, memory, event-driven workflows, human-in-the-loop systems, and multi-agent collaboration.

The course also provides a complete guide to Retrieval-Augmented Generation (RAG). You will implement traditional RAG, advanced RAG, hybrid search, contextual retrieval, multimodal RAG, and persistent memory systems. You will work with vector databases such as FAISS, Pinecone, ChromaDB, and Weaviate.

You will also learn LangSmith for debugging, monitoring, tracing, and optimizing AI applications.

Throughout the course, you'll build real-world projects including:

• Chatbots and AI Assistants

• Document Q&A Systems

• Resume Screening Agents

• Research Assistants

• Multi-Agent Applications

• Knowledge Base Chatbots

• Autonomous Task Automation Systems

• Multimodal AI Applications

• Production-Ready RAG Systems

By the end of this course, you'll have the practical skills needed to build, deploy, and optimize modern AI applications and become a proficient AI Engineer.

Whether you are a beginner, software developer, data scientist, ML engineer, or AI enthusiast, this course will provide everything you need to master Generative AI, Agentic AI, and RAG systems from scratch.

Who this course is for:

Individuals passionate about AI and ML who want to expand their knowledge and skills in generative AI applications.
Developers interested in integrating advanced AI capabilities into their applications and learning about the deployment and optimization of AI models
Professionals looking to enhance their expertise in building and deploying generative AI models, particularly using Langchain and Huggingface.
Tech Enthusiasts and Students eager to explore the next generation of AI application development with practical hands-on projects.
Data Scientists and Researchers looking to integrate agentic behavior into their data-driven projects.

Complete Generative AI, Agentic AI & RAG Bootcamp

What you'll learn

Explore related topics

Coding Exercises

Course content

Course Onboarding & Enterprise Environment Setup6 lectures • 6min

Python Development Environment Setup7 lectures • 36min

Python Core Fundamentals2 lectures • 1hr 10min

Python Functions and Modular Programming4 lectures • 40min

File Handling and Exception Handling4 lectures • 23min

Object-Oriented Programming9 lectures • 1hr 35min

Iterators, Generators, Closures, and Decorators6 lectures • 51min

Production Python: Logging & Pydantic3 lectures • 55min

Data Analysis with NumPy, Pandas & Matplotlib4 lectures • 1hr 47min

Building AI Apps with Streamlit5 lectures • 46min

Requirements

Description

Who this course is for: