Welcome to the Introduction to LangChain course. Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real. That's something that very few experts predicted, and it's essential to be prepared for the future.
Welcome to the Introduction to LangChain course. Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real. That's something that very few experts predicted, and it's essential to be prepared for the future.
LangChain is an amazing tool that democratizes machine learning for everybody. With LangChain, every software engineer can use machine learning and build applications with it. Prior to LangChain and LLMs, you needed to be an expert in the field. Now, you can build an application with a couple of lines of code. Think about language models as a layer between humans and software. LangChain is a tool that allows the integration of LLMs within a larger software.
Topics covered in that course:
LangChain Basics
Loading and Summarizing Data
Prompt Engineering Fundamentals
Vector Database Basics
Retrieval Augmented Generation
RAG Optimization and Multimodal RAG
Augmenting LLMs with a Graph Database
Augmenting LLMs with tools
How to Build a Smart Voice Assistant
How to Automate Writing Novels
How to Automate Writing Software
The course is very hands-on. We will work on many examples to build your intuition on the different concepts we will address in this course. By the end of the course, you will be able to build complex software applications powered by Large Language Models.
Warning: during the course, I used a lot of the OpenAI models through their API. If you choose to use the OpenAI API as well, be aware that this will generate additional costs. I expect that reproducing all the examples in the course should not require more than $50 in OpenAI credits. However, all the examples can be reproduced for free if you choose to use open-source LLMs.
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