Description
Price: 5.00 USD | Size: 2.06 GB | Duration: 5.17+ Hrs
BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD
Description
The course Zero to Hero in LangChain: Build GenAI apps using LangChain is your comprehensive guide to mastering LangChain, an innovative framework that simplifies building complex AI-based applications. This course will guide you from understanding the basics to implementing complex applications that use memory, Recovery Augmented Generation (RAG), tools, agents, and more.
What you will learn in the course
- Developing your first LangChain application and setting up a robust development environment.
- Master the use of Prompt patterns, Chains and Runnables to create diverse interactions with AI.
- Implement dynamic execution flows and output parsing to improve your AI models.
- Harnessing the power of memory in LangChain to build context-preserving conversational AI.
- Create a fully functional RAG pipeline to maximize the value of your data recovery processes.
- Build custom tools and agents and learn how to integrate them into your applications.
- Monitor and optimize your applications using LangSmith.
- Design suitable user interfaces for your AI applications with Streamlit.
This course is suitable for people who
- Forward-looking AI developers who want to build and deploy advanced Generative AI applications using LangChain.
- Data scientists aim to improve their AI models with memory, retrieval-augmented generation (RAG), and integration of custom tools.
- Software engineers looking to master LangChain to create dynamic and interactive AI-based applications.
- Tech enthusiasts who are eager to explore the latest frameworks and techniques to develop advanced AI solutions.
- AI researchers interested in using LangChain features to improve conversational AI and data retrieval systems.
- Product managers who want to understand the capabilities of LangChain to effectively lead AI-driven product development.
Zero to Hero in LangChain course prerequisites: Build GenAI apps using LangChain
- Basic Python knowledge, familiarity with AI concepts, and access to a computer with internet are recommended; no advanced AI experience required.
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