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所在平台: Udemy |
课程主页: https://www.udemy.com/course/becoming-an-ai-engineer-with-langchain/
课程评论：没有评论
课程名称：使用LangChain构建AI应用程序和代理 课程概述： "使用LangChain构建AI应用程序和代理"是一门实践性课程，旨在提供对LangChain的全面理解，该框架用于开发基于大型语言模型（LLMs）的应用。该课程由Mindify AI的创始人Mark Chen主讲，涵盖从生成式AI基础知识到LangChain高级组件和集成的内容。学完后，您将拥有构建基于LangChain的应用程序的实践经验，能够优化数据处理、模型交互和AI部署流程。 讲师简介： Mark Chen是Mindify AI的创始人，经验丰富的AI工程师和企业家，专注于创建生成式AI解决方案。他在构建基于LLM的应用程序、开发基于AI代理的应用以及掌握LangChain框架方面具有丰富的经验。本课程将结合基础知识和前沿AI技术的见解，以独特的实践视角进行教学。 课程大纲： - 第1章：生成式AI和LangChain简介 - 第2章：与LLMs的合作 - 从嵌入到聊天模型 - 第3章：文档处理 - 在LangChain中使用文档加载器 - 第4章：数据存储 - 向量数据存储与上下文检索 - 第5章：基本工具 - LangChain工具和代码集成 - 第6章：代理与决策 - LangGraph代理应用 - 第7章：LangChain平台集成 - 跨平台集成LLMs - 第8章：构建应用程序 - 面向聊天机器人、RAG和代理模型的LangChain API 课程学习成果： - 理解LangChain的架构：熟悉其结构、组件和模块化整合。 - 掌握提示工程：学习零-shot、few-shot和思维链提示，以提高模型的准确性和实用性。 - 实现现实世界的应用：创建处理文档、搜索数据的LLM应用程序，并通过自定义代理进行交互。 - 与AI模型对接：学习如何利用LangChain的API在可部署的应用程序中使用聊天模型、数据存储和代理。 适合人群： 本课程特别适合以下人群： - 希望获得基于LLM应用程序实际经验的有志AI工程师和开发者。 - 有意向通过构建实际应用程序转型至AI的软件工程师。 - 希望深入理解生成式AI和LangChain框架的技术爱好者与研究者。 - 任何希望利用LLM和AI代理的力量来构建健壮、可扩展应用程序的人。 报名参加本课程，启动您的AI工程师之旅，掌握创造现实世界应用的技能，推动AI的边界。
Building AI Applications and Agents with LangChainAbout the Course "Building AI Applications and Agents with LangChain" is a hands-on course designed to provide a thorough understanding of LangChain, a robust framework for developing applications with large language models (LLMs). Led by Mark Chen, founder of Mindify AI, this course is crafted to take you from the basics of generative AI to advanced LangChain components and integrations. By the end, you'll have practical experience building applications that use LangChain to streamline data handling, model interactions, and AI deployment processes.About the Instructor Mark Chen, the founder of Mindify AI, is an experienced AI engineer and entrepreneur dedicated to creating generative AI solutions. His expertise spans building LLM-driven applications, developing AI agent-based applications, and navigating the LangChain framework. Mark's background in developing real-world AI applications gives this course a unique, practical focus that combines foundational knowledge with insights from the cutting edge of AI technology.Course Outline - Chapter 1: Introduction to Generative AI and LangChain - Chapter 2: Working with LLMs - From Embedding to Chat Models - Chapter 3: Document Handling - Using Document Loaders in LangChain - Chapter 4: Data Storage - Vector Data Stores and Context Retrieval - Chapter 5: Essential Tools - LangChain Tooling and Code Integration - Chapter 6: Agents and Decision-Making - LangGraph Agent Applications - Chapter 7: LangChain on Platforms - Integrating LLMs across platforms - Chapter 8: Building Applications - LangChain APIs for Chatbots, RAG, and Agentic Models What Will You Learn from This Course Understand the Architecture of LangChain: Get familiar with its structure, components, and modular integrations. - Master Prompt Engineering: Learn zero-shot, few-shot, and chain-of-thought prompting to improve model accuracy and utility. - Implement Real-World Applications: Create LLM applications that handle documents, search data, and interact through custom agents. - Interface with AI Models: Learn how to utilize LangChain's APIs for chat models, data stores, and agents in deployable applications.Who Will Be Suitable for This Course This course is ideal for: - Aspiring AI Engineers and Developers who want hands-on experience with LLM-driven applications. - Software Engineers interested in transitioning to AI by building practical applications with a comprehensive framework. - Tech Enthusiasts and Researchers looking to deepen their understanding of generative AI and LangChain's framework. - Anyone interested in AI development who wants to leverage the power of LLMs and AI agents to build robust, scalable applications. Take this course to kickstart your journey as an AI engineer and gain the skills to create real-world applications that push the boundaries of what AI can achieve.
