Learn all the basics of LangChain by building LLM-powered applications with OpenAI, Build Web Apps with Streamlit
- New
- Rating: 4.5 out of 54.5 (18 ratings)
- 838 students
- 1hr 47min of on-demand video
- Created by Muhammad Moin
- English
What you’ll learn
- How to Use LangChain to Build LLM-Powered Applications
- Deep Dive into LangChain
- How to build an end to end LangChain based Generative AI application
- Learn about LangChain components, including LLM wrappers, prompt templates, chains, and agents.
- Project: Streamlit Application with LangChain and OpenAI for Instant Book Insights
Requirements
- Basic Python programming experience is required
- You should be able to sign up to OpenAI API with a valid phone number
Description
Welcome to first LangChain course
This comprehensive course is designed to teach you how to use the LangChain library for building LLM-powered applications.
This course will equip you with the skills and knowledge necessary to develop LLM solutions for a wide range of topics.
The course starts with Introduction to Generative AI, which includes What is Generative AI, What are Discriminative Model and Generative Models and the Training Process of Generative Models.
In the Section 2, Introduction to Large Language Models is presented, which includes What are Large Language Models, Large Language Models Architecture, Difference between Traditional Machine Learning Models and Large Language Models, Use cases of Large Language Models, the lecture also covers, prompt design. Along with this, it also covers Zero Shot Learning and Few Shot Learning.
The Section 3 covers LangChain, What is LangChain, this section also covers OpenAI Large Language Models GPT 3.5, GPT 4, the limitations of these Large Language Models and how does LangChain overcomes these limitations.
The Section 4 covers LangChain, Large Language Models, Prompt Templates, Simple Sequential Chain, Sequential Chain, Agents & Memory.
In the Section 5, a Streamlit Application with LangChain and OpenAI for Instant Book Insights is been build.
The topics covered in this course include:
- Generative AI
- Large Language Models
- LangChain
- Prompts, PromptTemplates
- Chains: SequentialChain, LLMChain
- Agents
- Memory
- Streamlit (for UI)
Along with lifetime access to the course, you’ll get:
- Dedicated 1 on 1 troubleshooting support with me
- No extra cost for continuous updates and improvements to the course
Who this course is for:
- Python programmers who want to build LLM-Powered Applications using LangChain
- Any programmer interested in AI.
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Course content
5 sections • 5 lectures • 1h 47m total lengthCollapse all sections
Generative AI1 lecture • 14min
- What is Generative AI13:50
Large Language Models1 lecture • 8min
- Introduction to Large Language Models07:38
LangChain1 lecture • 10min
- What is LangChain09:34
Deep Dive into LangChain1 lecture • 42min
- Large Language Models, Prompt Templates, Sequential Chain, Agents & Memory41:35
Project1 lecture • 35min
- Book Summaries: Streamlit Application with LangChain & OpenAI for Book Insights34:50
