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 Code

Learn 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, dict

Learn 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 data

Understand 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 biases

Learn how to build Generative AI applications Using LangChain, RAG

Learn what is RAG(Retrieval-Augmented Generation) and deep dive on preparing input, chunking methods, embeddings, vector store, similarity search, RAG pipeline

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

Learn different Text Chunking Methods in RAG Systems and how to choosing a chunking Method

Character Text Splitter Chunking Method

Recursive Character Text Splitter Chunking Method

Markdown Header Text Splitter Chunking Method

Token Text Splitter Chunking Method

Learn what is Prompt Engineering

Learn how to create OpenAI account and how to generate API key

Learn different prompt engineering techniques

Basic prompt

Role Task Context Prompt

Few shot Prompting

Chain of thought Prompting

Constrained Output Prompting

Understand Document Loaders - CSVLoader, HTMLLoader, PDFLoaders

Learn how to provide memory to Large Language Models(LLM)

Learn different memory types - ConversationBufferMemory, Conversation Buffer Window, ConversationSummaryMemory

Learn how to chain different LangChain components

Learn different chains - Single Chain, Simple Sequential Chain, Sequential Chain, Math Chain, RAG Chain, Router Chain, LLM Router Chain, SQL Chain

Learn how to build multi agentic frameworks using CrewAI and LangChain tools

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

Who this course is for:
- Developers interested in building Generative AI applications using LangChain, RAG.
- Programmers interested in building multi agentic frameworks.
- AI engineers and data scientists.
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