Langchain for beginners : Build GenAI LLM Apps in Easy Steps

What you’ll learn:
Learn what LangChain is how it simplifies using LLMs in our applications
Use OpenAI LLMS in a python application
Use Open Source LLMS like Mistral,Gemma in a python application
Run Open Source LLMs on your local machine using OLLAMA
Use PromptTemplates to reuse and build dynamic prompts
Understand how to use the LangChain expression language
Create Simple and Regular Sequential chains using LCEL
Work with multiple LLMs in a single chain
Learn why and how to maintain Chat History
Learn what embeddings are and use the Embeddings Model to find text Similarity
Understand what a Vector Store is and use it to store and retrieve Embeddings
Understand the process of Retrieval Augmented Generation(RAG)
Implement (RAG) to use our own data with LLMs in simple steps
Analyze images using Multi Modal Models
Build multiple LLM APPs using Streamlit and LangChain
All in simple steps

Requirements:
Knowledge of Python
OpenAI Account to work with OpenAI LLMs

Description:
Welcome to LangChain for Beginners!This course is designed to provide a gentle, step-by-step introduction to LangChain, guiding youfrom the basics to more advanced concepts. Whether you’re a complete novice or have someexperience with AI, this course will help you understand and leverage the power of LangChain forbuilding AI-powered applications.Course Goals:- Gradual Learning: Learn LangChain gradually from basic to advanced topics with clear andconcise instructions.- Comprehensive Understanding: Understand why LangChain is a powerful tool for building AIapplications and how it simplifies the integration of language models into your projects.- Hands-On Experience: Gain practical experience with essential LangChain features such asprompt templates, chains, agents, document loaders, output parsers, and model classes.What You Will Learn:- Introduction to LangChain: Get started with the basics of LangChain and understand its coreconcepts.- Building Blocks of LangChain: Learn about prompt templates, chains, agents, document loaders,output parsers, and model classes.- Creating AI Applications: See how these features come together to create a smart and flexible- Practical Coding: Write and run code examples to get a hands-on sense of how LangChaindevelopment looks like.Course Structure:- Concise Chapters: Each chapter focuses on a specific topic in LangChain programming,ensuring you gain a deep understanding of each concept.- Interactive Learning: Code along with the examples provided to reinforce your learning and buildyour skills.By the end of this course, you will:Learn what LangChain is how it simplifies  using LLMs in our applicationsUse OpenAI LLMs in a python applicationUse Open Source LLMs like Mistral,Gemma in a python applicationRun Open Source LLMs on your local machine using OLLAMAUse PromptTemplates to reuse and build dynamic prompts Understand how to use the LangChain expression languageCreate Simple and Regular Sequential chains using LCEL Work with multiple LLMs in a single chainLearn why and how to maintain Chat HistoryLearn what embeddings are and use the Embeddings Model to find text SimilarityUnderstand what a Vector Store is and use it to store and retrieve EmbeddingsUnderstand the process of Retrieval Augmented Generation(RAG) Implement  (RAG) to use our own data with LLMs in simple stepsAnalyze images using Multi Modal ModelsBuild multiple LLM APPs using Streamlit and LangChainAll in simple steps

Who this course is for:
Python Developers who want to use LangChain to build GenAI LLM applications
Any students who has completed my Python or OpenAI course and who want to master LanChain