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Langchain for beginners : Build GenAI LLM Apps in Easy Steps | Udemy [Update 02/2025]
English | Size: 1.6 GB
Genre: eLearning[/center]

A Step-by-Step Guide to Master LangChain

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

LangChain has quickly become one of the most important frameworks for building real-world applications using large language models (LLMs). This course is designed to help you get started with LangChain and progressively master its powerful features, all through clear and simple examples.

Whether you're a Python developer, an AI enthusiast, or someone curious about LLMs, this course will give you the tools and confidence to build intelligent applications using both OpenAI and open-source models.

What You'll Learn

• What LangChain is and how it simplifies integrating LLMs into applications

• Use OpenAI LLMs in Python to generate and process natural language

• Use open-source LLMs like Mistral and Gemma in your own apps

• Run open-source models locally on your machine using Ollama

• Build dynamic prompts using PromptTemplates

• Understand and apply the LangChain Expression Language (LCEL)

• Create simple and regular sequential chains to control workflow logic

• Use multiple LLMs within a single chain for flexible responses

• Maintain and use chat history to create context-aware apps

• Learn about embeddings and apply them to measure text similarity

• Understand vector stores and use them to store and search embeddings

• Learn the Retrieval-Augmented Generation (RAG) workflow

• Implement RAG with your own data using LangChain in simple steps

• Analyze images using multi-modal models

• Build real-world LLM-powered apps using Streamlit and LangChain

Who This Course Is For

• Python developers exploring AI and LLM integration

• Anyone looking to build chatbots, assistants, or smart tools using LLMs

• Professionals working on NLP, search, RAG, or agentic workflows

• Students, hobbyists, or beginners interested in AI application development

Prerequisites

• Basic understanding of Python

• No prior experience with LLMs or LangChain needed - everything is taught step by step

By the End of This Course, You Will Be Able To:

• Confidently use LangChain to work with OpenAI and open-source models

• Structure and build LLM workflows using chains and tools

• Implement powerful features like RAG, chat history, and image understanding

• Deploy fully functional apps using Streamlit and LangChain

• Build your own intelligent apps using both cloud and local LLMs

If you've been wanting to learn how to work with LLMs in your own projects - using simple steps and real examples - this is the perfect course to get started.

Enroll now and bring your LLM ideas to life using LangChain.

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

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