Langchain for beginners - Build GenAI LLM Apps in Easy Steps

Langchain for beginners : Build GenAI LLM Apps in Easy Steps, available at $44.99, has an average rating of 4.75, with 70 lectures, 6 quizzes, based on 8 reviews, and has 129 subscribers.

You will learn about 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 This course is ideal for individuals who are Python Developers who want to use LangChain to build GenAI LLM applications or Any students who has completed my Python or OpenAI course and who want to master LanChain It is particularly useful for Python Developers who want to use LangChain to build GenAI LLM applications or Any students who has completed my Python or OpenAI course and who want to master LanChain.

Enroll now: Langchain for beginners : Build GenAI LLM Apps in Easy Steps

Summary

Title: Langchain for beginners : Build GenAI LLM Apps in Easy Steps

Price: $44.99

Average Rating: 4.75

Number of Lectures: 70

Number of Quizzes: 6

Number of Published Lectures: 70

Number of Published Quizzes: 6

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 83

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

Welcome to LangChain for Beginners!

This course is designed to provide a gentle, step-by-step introduction to LangChain, guiding you

from the basics to more advanced concepts. Whether you’re a complete novice or have some

experience with AI, this course will help you understand and leverage the power of LangChain for

building AI-powered applications.

Course Goals:

– Gradual Learning: Learn LangChain gradually from basic to advanced topics with clear and

concise instructions.

– Comprehensive Understanding: Understand why LangChain is a powerful tool for building AI

applications and how it simplifies the integration of language models into your projects.

– Hands-On Experience:Gain practical experience with essential LangChain features such as

prompt 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 core

concepts.

– 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 LangChain

development 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 build

your skills.

By the end of this course, you will:

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: How to make the best

Lecture 3: Download Completed Project

Lecture 4: Download Prompts

Chapter 2: The Fundamentals

Lecture 1: What is GenAI

Lecture 2: What is OpenAI

Lecture 3: Other LLMs

Lecture 4: What is Langchain

Chapter 3: Software Setup

Lecture 1: Setup OpenAI Account

Lecture 2: Setup API Key

Lecture 3: Setup Open Source LLMs

Chapter 4: Langchain in action

Lecture 1: Setup Project

Lecture 2: Langchain in action

Lecture 3: Use Open Source Models Locally

Lecture 4: What is Streamlit

Lecture 5: Use Streamlit GUI

Lecture 6: Turn on Debug

Chapter 5: Prompt Templates

Lecture 1: Introduction

Lecture 2: PromptTemplate in action

Lecture 3: Add two more place holders

Lecture 4: Improve the prompt

Lecture 5: Create a Travel Guide App

Chapter 6: Chains

Lecture 1: Introduction

Lecture 2: LCEL In Action

Lecture 3: UseCase and Code Walkthrough

Lecture 4: Simple Sequential Chain

Lecture 5: Display the title

Lecture 6: Using Multiple LLMs

Lecture 7: Sequential Chain

Lecture 8: Format Output

Lecture 9: Organize Files

Chapter 7: Maintaining ChatHistory

Lecture 1: Introduction

Lecture 2: Use ChatPromptTemplate

Lecture 3: Code Walk Through

Lecture 4: Use StreamlitChatMessageHistory

Lecture 5: Display History

Lecture 6: Use ChatMessageHistory

Chapter 8: Embeddings

Lecture 1: Introduction

Lecture 2: Using the Embeddings Model

Lecture 3: Similarity Finder

Chapter 9: Vector Stores

Lecture 1: Introduction

Lecture 2: Code Walk Through

Lecture 3: Implement Job Search Helper

Lecture 4: Test

Lecture 5: Use Retriever

Chapter 10: RAG – Working With Documents

Lecture 1: What is RAG

Lecture 2: UseCase and Code Walkthrough

Lecture 3: Implement RAG Part 1

Lecture 4: Implement RAG Part 2

Lecture 5: Test

Lecture 6: History Aware RAG Bot

Lecture 7: Test

Lecture 8: Work with other File Formats

Chapter 11: Image Processing

Lecture 1: Introduction

Lecture 2: Create Image Analyzer App

Lecture 3: Use Streamlit

Lecture 4: KYC Usecase

Lecture 5: KYC Part 1

Lecture 6: KYC Part 2

Lecture 7: Test

Chapter 12: Agents

Lecture 1: Introduction

Lecture 2: Code Walk Through

Lecture 3: Setup Project

Lecture 4: Create an Agent

Lecture 5: Test

Chapter 13: Deployment

Lecture 1: Introduction

Lecture 2: Update Code

Lecture 3: Push to GitHub

Lecture 4: Deploy

Chapter 14: Bonus Lecture

Lecture 1: Wrap Up

Instructors

Bharath Thippireddy
IT Architect and Best Selling Instructor- 700000+ students

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5 stars: 6 votes

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