Complete Guide Of Generative Ai: Langchain, Agentic Ai, Rag

Complete reference of Gen AI with fundamentals of NLP, LangChain, LCEL, LangSmith, LangServe, Agentic AI, RAG, Neo4J

What you'll learn

Master the fundamentals of NLP: Tokenization, embedding, POS tagging, TF-IDF, chunking, and more.

Understand the fundamentals of Generative AI: Explore key concepts like autoencoders, VAEs, GANs, and Transformer models

Master Prompt Engineering: Learn techniques to design effective prompts for models like ChatGPT, including zero-shot, one-shot, and few-shot prompting.

Work with industry-leading tools: Explore cutting-edge Generative AI platforms like ChatGPT, Google Gemini, and Microsoft CoPilot for real-world applications.

Set up the environment for hands-on Generative AI applications: Implement RAG using Python, VS Code, and LangChain.

Work with LangChain and LangChain Ecosystem Libraries (LCEL): Build real-world Generative AI applications and explore the LangChain ecosystem.

Develop AI Agents: Understand and implement agents like Crew AI and AutoGen to automate complex tasks.

Implement Vector RAG and Graph RAG: Use Neo4j for advanced retrieval and data augmentation techniques.

Learn Self-Reflective RAG techniques: Understand how AI can reason and reflect on its own processes.

Practical Python skills for Generative AI: Start from the basics and progress to advanced AI development with Python and libraries like NLTK.

Build AI solutions from the ground up: Gain end-to-end knowledge of Generative AI, from basics to advanced implementations with LangChain and LCEL.

Requirements

Basic understanding of Python but dont worry the course will cover fundamental of Python.

Description

Unlock the full potential of Generative AI in this comprehensive, hands-on course tailored for students, developers, and AI enthusiasts. Whether you're a beginner or looking to deepen your expertise, this course offers an immersive experience, starting with the Fundamentals of Natural Language Processing (NLP) and Generative AI, giving you the foundational knowledge needed to excel. You will learn the basics of Python, ensuring even those new to programming can participate fully. From there, we dive into advanced LangChain implementations, where you'll build real-world applications. You'll also gain practical experience with LangSmith and LangGraph, key tools in the AI ecosystem.Explore the power of AI Agents, including Crew AI and AutoGen, and see how these autonomous systems can transform tasks like customer service, automation, and more. The course also covers cutting-edge Retrieval-Augmented Generation (RAG) techniques, including Vector RAG and Graph RAG using Neo4j for enhanced search and data retrieval. A special focus on Self-Reflective RAG will introduce you to the next frontier of AI-driven reasoning.With quizzes, practical coding challenges, and hands-on projects, this course ensures you gain both theoretical understanding and practical experience in the most important areas of Generative AI. Get ready to build AI solutions from the ground up!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Fundamental of Gen AI

Lecture 2 Overview of Generative AI: Easy explanation, Gen AI vs Predective AI

Lecture 3 Generative AI - Models: Latent Space

Lecture 4 Gen AI Models: Auto Encoder and VAE

Lecture 5 Gen AI Models : GANs model

Section 3: Fundamental of NLP

Lecture 6 What is NLP?

Lecture 7 NLP concepts: POS, NER, Chunking, BOW, TF-IDF and Embeddings

Lecture 8 NLP concepts: Tokenization, Stemming, Lemmatization

Lecture 9 NLP concepts: Evaluation of NLP

Section 4: Environment Setup

Lecture 10 Python, VS Code, Neo4J, API Key setup

Section 5: Python (for Beginners)

Lecture 11 Python basics : Hello World, Data Type, If-Else

Lecture 0 Python basics : List, Tuples, Set, Dictionary

Lecture 12 Python basics : Develop first LLM app

Section 6: NLTK - Natural Language ToolKit : Understand NLP concept with Python

Lecture 13 NLTK - Embedding , Tokenization

Lecture 14 NLTK - BOW, TF-IDF

Section 7: Gen AI products and Prompt Engineering

Lecture 15 Prompt Engineering: Concepts, Key Elements, Different techniques

Lecture 16 Prompt engineering hands on with ChatGPT

Lecture 17 Prompting through Groq UI

Lecture 18 Prompting through Gemini

Lecture 19 Gen AI through Microsoft Co Pilot

Section 8: LangChain

Lecture 20 Concept of LangChain

Lecture 21 Overview of LCEL(LangChain Expression Language)

Lecture 22 Quick hands-on with LCEL

Lecture 23 First LLM Application with LangChain

Lecture 24 First Streamlit Chatbot with LangChain

Section 9: LangGraph

Lecture 25 LangGraph - Concept and Hands on

Section 10: Concept of Agentic AI

Lecture 26 Agentic AI - Concept and workflow

Lecture 27 Agentic AI - Overview and Key characteristics

Lecture 28 Agentic AI - Applications

Lecture 29 Agentic AI - Design Pattern

Section 11: CrewAI

Lecture 30 Crew AI - Overview and components

Lecture 31 Crew AI Hands-On : Build simple one agent streamlit app

Lecture 32 Crew AI Hands-On: Hierarchical Process

Lecture 33 Crew AI Hands-On: Customized Manager Agent

Lecture 34 Crew AI Hands-On : Build Trip Planner Agentic app with Streamlit

Lecture 35 Crew AI Hands-On : Build Game Python code with Agent

Lecture 36 AgentOps : Integrate Trip Planner Agents

Section 12: AutoGen

Lecture 37 AutoGen - Overview and concepts

Lecture 38 AutoGen Hands-On : Overview

Lecture 39 AutoGen Hands-On : Execute code with agent

Lecture 40 AutoGen Hands-On: Sequential Pattern

Lecture 41 AutoGen Hands-On: GroupChat Pattern

Lecture 42 AutoGen Hands-On: Two Agents Chat with Streamlit

Lecture 43 AutoGen Hands-On: How to create custom tools

Lecture 44 AutoGen Hands-On: Agentic RAG with streamlit

Lecture 45 AutogenStudio : Microsoft product to build Agents through UI

Section 13: Fundamentals of RAG

Lecture 46 Why RAG?

Lecture 47 Process of RAG

Section 14: Implement chatbot with Vector RAG

Lecture 48 What is vector RAG ?

Lecture 49 Develop vector RAG with Groq API and Langchain

Section 15: Implement RAG chatbot with Graph RAG

Lecture 50 What is Graph RAG

Lecture 51 Graph RAG with Neo4j

Lecture 52 Hybrid search Graph RAG with Neo4j

Section 16: Implement Self-Reflective RAG or Adaptive RAG

Lecture 53 Understand adaptive or self-reflective flow

Lecture 54 Implement Self-reflective RAG chatbot with Langgraph

Section 17: LangSmith

Lecture 55 LangSmith integration with RAG

Section 18: Assignment and Quiz

Lecture 56 Assignment

Data Scientists,Machine Learning Engineers,AI and NLP Enthusiasts,Developers and Software Engineers,Researchers and Academics,Product Managers and Technical Leads,Students and Learners,AI Practitioners and Consultants,Quality Engineers