Published 11/2024
Created by Fikayo Adepoju
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 30 Lectures ( 4h 20m ) | Size: 1.73 GB
Getting Started with LangChainJS
What you’ll learn
How to Design powerful prompts to query A.I models
How to ntegrate with powerful models like Gemini models from Google and GPT models from OpenAI
Build conversational chat interactions with A.I models
Use A.I to process images
Load data from local files or specific web pages
Generate results based on information from the web and PDF files
Requirements
Basic Programming Skills
Basic Understanding of Javascript
Basic Understanding of Nodejs
Description
The sole purpose of this course is to get your feet wet and your hands building with LangChainJSLangChainJS gives Web and JavaScript Developers to ability to add A.I-powered features to their web applications using best-in-class Large Language Models from OpenAI, Google, Anthropic and many more LLM vendors out there.This is the perfect course to get yourself into A.I Engineering and A.I development with LangChain, the biggest A.I software development toolchain in the market today.By the end of this course, you will have the skills to:Design powerful prompts to query A.I modelsIntegrate with powerful models like Gemini 1.5 Flash from Google Generative AI and GPT 4o by OpenAIBuild conversational chat interactions with A.I modelsUse A.I to process imagesControl the creativity of the model and how much tokens it consumesTrack token consumptionLoad data from local files or specific web pagesGenerate results based on information from the webGenerate results based on information from your PDF filesSo, if anything in the above list sounds fun, let’s get started.Also, feel free to inbox me about any new chapter you will like me to work on. I’ll appreciate your suggestions
Who this course is for
Web Developers who want to add AI-powered features to their applications
Developers who want to transition into A.I Engineering
Developers who want to improve the performance of model-powered features with private data
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