Generative Ai Bootcamp

Build Generative AI applications using LangChain, RAG. Build multi agentic AI systems using Crew AI. Master LLMs.

What you'll learn

Learn to build Generative AI applications using LangChain. Understand how to use LangChain components.

Learn to build multi agentic systems using Crew AI and LangChain tools. Deep dive different components of Crew AI.

Learn to build Retrieval-Augmented Generation (RAG) pipelines - preparing input, chunking methods, embeddings, vector store, similarity search, RAG pipeline

Learn prompt engineering techniques with practical implementation - Basic, Role Task Context, Few shot, Chain of thought, Constrained Output Prompting

Learn chains with practical implementation - Single, Simple Sequential, Sequential, Math, RAG, Router, LLM Router, SQL Chains and many more

Learn document Loaders with practical implementation - CSVLoader, HTMLLoader, PDFLoaders and many more

Learn Hugging Face and how to use the models from Hugging Face and build Generative AI applications

Learn different Text Chunking Methods in RAG Systems - Character Text Splitter, Recursive Character Text Splitter, Markdown Header, Token Text Splitter Chunking

Learn vector Databases for RAG Systems: Pinecone, Chroma, Weaviate, Milvus, FAISS

Understand the terminology - Artificial intelligence, Machine Learning, Deep Learning and Generative AI.

Understand the attention mechanism and how transformers encode and decode data.

Understand Foundation Models, history, Applications, types, examples of foundation models.

Understand Language Model Performance; Top Open-Source LLMs; How to Select the right Foundation Model. And, responsible AI practices and the importance of addre

Learn memory types with practical implementation - ConversationBufferMemory, Conversation Buffer Window, ConversationSummaryMemory and many more

Requirements

We cover Python basics but prefer to have familiarity with the Python programming language.

Access to a computer with good internet connection.

Have access to OpenAI, Claude Anthropic, or you can use open source models

Basic understanding on using different code editors - Jupyter notebook, VScode, etc.

Description

Learn how to download and install Anaconda Distribution, Jupyter notebook, Visual Studio CodeLearn how to use Jupyter notebook 'Markdown' features Learn how to install CUDA Toolkit, cuDNN, PyTorch and how to enable GPU Learn Python basics - Introduction, Package Installation, Package Import, Variables, Identifiers, Type conversion, Read input from keyboard, Control statements and Loops, Functions, string, Data Structures - list, tuple, set, dictLearn what is Artificial intelligence, Machine Learning, Deep Learning and Generative AI; And, the history of AI;Understand the attention mechanism and how transformers encode and decode dataUnderstand what are the Foundation Models, history, Applications, types, examples of foundation models.Understand Language Model Performance; Top Open-Source LLMs; How to Select the right Foundation Model?Learn Responsible AI practices and the importance of addressing biasesLearn how to build Generative AI applications Using LangChain, RAGLearn what is RAG(Retrieval-Augmented Generation) and deep dive on preparing input, chunking methods, embeddings, vector store, similarity search, RAG pipelineUnderstand Vector Databases for RAG Systems: Pinecone, Chroma, Weaviate, Milvus, FAISSLearn different Text Chunking Methods in RAG Systems and how to choosing a chunking MethodCharacter Text Splitter Chunking MethodRecursive Character Text Splitter Chunking MethodMarkdown Header Text Splitter Chunking MethodToken Text Splitter Chunking MethodLearn what is Prompt EngineeringLearn how to create OpenAI account and how to generate API keyLearn different prompt engineering techniquesBasic promptRole Task Context PromptFew shot PromptingChain of thought PromptingConstrained Output PromptingUnderstand Document Loaders - CSVLoader, HTMLLoader, PDFLoadersLearn how to provide memory to Large Language Models(LLM)Learn different memory types - ConversationBufferMemory, Conversation Buffer Window, ConversationSummaryMemory Learn how to chain different LangChain componentsLearn different chains - Single Chain, Simple Sequential Chain, Sequential Chain, Math Chain, RAG Chain, Router Chain, LLM Router Chain, SQL ChainLearn how to build multi agentic frameworks using CrewAI and LangChain toolsLearn what is Hugging Face and how to use the models from Hugging Face and build Generative AI applications

Overview

Section 1: Course Overview

Lecture 1 Course Overview

Section 2: Software Installation and Environment Setup

Lecture 2 Download and install Anaconda Distribution

Lecture 3 Jupyter Notebook installation and overview

Lecture 4 Jupyter Notebook 'Markdown' features deep dive

Lecture 5 Download and install Visual Studio Code

Lecture 6 Enable GPU – Install CUDA Toolkit, cuDNN, PyTorch

Section 3: Python Crash Course

Lecture 7 Intro, Package Installation & Import, Variables, Identifiers, Type conversion

Lecture 8 Control statements and Loops, Functions

Lecture 9 Data Structures - list

Lecture 10 Data Structures - tuple

Lecture 11 Data Structures - string

Lecture 12 Data Structures - set

Lecture 13 Data Structures - dictionary

Section 4: Introduction to AI, Machine Learning, Generative AI; Transformer architecture

Lecture 14 Introduction to AI, Machine Learning, Deep Learning and Generative AI

Lecture 15 History of AI

Lecture 16 Understanding the attention mechanism, Encoder-Decoder Models-Encoder deep dive

Lecture 17 Understanding the attention mechanism, Encoder-Decoder Models-Decoder deep dive

Section 5: Understand Foundation Models and Responsible AI practices

Lecture 18 History Of Foundation Models

Lecture 19 What are Foundation Models?

Lecture 20 Applications Of Foundation Models

Lecture 21 Types of Foundation Models

Lecture 22 Examples of Foundation Models

Lecture 23 LLM Benchmarks: Model Performance; Top Open-Source LLMs; Select right model

Lecture 24 Ethical AI: Responsible AI practices and the importance of addressing biases

Section 6: LangChain

Lecture 25 LangChain introduction

Lecture 26 LangChain components deep dive

Section 7: RAG(Retrieval-Augmented Generation)

Lecture 27 RAG : input, chunking, embeddings, vector store, similarity search, RAG pipeline

Lecture 28 Building a question-answering system using RAG

Lecture 29 Vector Databases for RAG Systems: Pinecone, Chroma, Weaviate, Milvus, FAISS

Section 8: Understanding Text Chunking Methods in RAG Systems

Lecture 30 Understanding Text Chunking Methods in RAG Systems

Lecture 31 Best Practices for Choosing a Chunking Method

Lecture 32 Character Text Splitter Chunking Method Demo

Lecture 33 Recursive Character Text Splitter Chunking Method Demo

Lecture 34 Markdown Header Text Splitter Chunking Method Demo

Lecture 35 Token Text Splitter Chunking Method Demo

Section 9: Prompt Engineering

Lecture 36 Prompt Engineering Introduction, Create OpenAI account and generate API key

Lecture 37 Basic prompt Demo-response to customer messages as a customer support specialist

Lecture 38 Role Task Context Prompt-response to customer messages as a customer specialist

Lecture 39 Few shot Demo - reply to customer messages as a customer support specialist

Lecture 40 Chain of thought Demo-response to customer messages as a customer specialist

Lecture 41 Constrained Output Prompting-response to customer messages as a customer special

Section 10: Document Loaders

Lecture 42 Document Loaders - CSVLoader, HTMLLoader, PDFLoader Demo

Section 11: Memory

Lecture 43 Memory Introduction

Lecture 44 ConversationBufferMemory with Demo

Lecture 45 Conversation Buffer Window Memory with Demo

Lecture 46 ConversationSummaryMemory with Demo

Section 12: Chains

Lecture 47 Chains Introduction

Lecture 48 Single Chain with Demo(Make up a funny company name for a product based company)

Lecture 49 SimpleSequentialChain with Demo(Write a blog post)

Lecture 50 SequentialChain with Demo(Employee performance review personalized plan)

Lecture 51 SequentialChain with Demo(Thought provoking questions on an academic topic)

Lecture 52 MathChain - Demo1

Lecture 53 MathChain - Demo2

Lecture 54 RAG Chain with Demo

Lecture 55 RouterChain with Demo

Lecture 56 LLMRouter Chain with Demo

Lecture 57 SQL Chain with Demo

Section 13: Build multi agentic systems using Crew AI and LangChain tools

Lecture 58 Multi Agentic Frameworks Introduction

Lecture 59 CrewAI Introduction

Lecture 60 CrewAI components deep dive

Lecture 61 CrewAI tools and LangChain tools

Lecture 62 Tools and Agents: Project #1 Web scraping

Lecture 63 Tools and Agents: Project #2 Personalized Email Drafts

Lecture 64 Tools and Agents: Project #3 Build Trading platform

Lecture 65 Tools and Agents: Project #4 Web scraping using Apify

Lecture 66 Tools and Agents: Project #5 Math Tools and Agents

Lecture 67 Tools and Agents: Project #6 SQL Database Tools and Sql Agent

Section 14: Hugging Face: Build GenAI applications using models from Hugging Face

Lecture 68 Hugging Face Introduction, Project #1 Sentence summarization

Lecture 69 Hugging Face Project #2 Image understanding

Lecture 70 Hugging Face Project #3 : Sentence translation pipeline using Transformer

Lecture 71 Hugging Face Project #4 : Summarization pipeline using Transformers Library

Lecture 72 Hugging Face Project #5: Sentence embeddings

Developers interested in building Generative AI applications using LangChain, RAG.,Programmers interested in building multi agentic frameworks.,AI engineers and data scientists.