Building Generative AI-Powered Applications with Python

所在平台: Coursera

课程主页: https://www.coursera.org/learn/building-gen-ai-powered-applications

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课程简介

课程名称：使用 Python 构建生成式 AI 应用程序 课程概述：准备好参与互动学习体验，构建真实世界的生成式 AI 应用程序和聊天机器人了吗？ 在这门动手实践的课程中，您将使用 Python、Flask、Gradio 和 LangChain 开发一系列指导项目，以创建适用于实际场景的 AI 驱动应用程序，包括语音助理、会议摘要器、语言翻译器以及个性化职业教练。您将与流行的大型语言模型（LLMs）如 GPT-3、Llama 2 和 Flan-UL2 一起工作。 课程大纲： 1. 生成式 AI 图像描述： - 学习生成式 AI 和基础模型的基本原理，掌握现代 AI 应用的驱动因素。 - 通过使用 BLIP 模型进行图像描述，获得实践经验，并利用 Gradio 构建互动用户界面。 - 使用 Hugging Face 访问预训练模型和数据集，并学习如何通过 IBM Code Engine 部署 AI 应用。 2. 创建 ChatGPT 类似的网站： - 学习使用生成式 AI 工具构建自己的 ChatGPT 应用。 - 利用 Facebook 的 BlenderBot 模型，使用 Hugging Face 的 Transformers 库进行实践，探索大型语言模型、提示工程和用户界面设计。 - 完成项目后，能够创建和定制自己的对话式 AI 网站。 3. 创建语音助理： - 探索如何通过结合 OpenAI 的 GPT-3 和 IBM Watson 的语音识别与合成服务，构建生成式 AI 驱动的语音助理。 - 学习应用结构、使用 Docker 进行容器化部署，并实现能够理解语音输入并自然回应的语音助理。 4. 生成式 AI 驱动的会议助理： - 构建能够转录、总结和回答会议内容问题的会议助理，了解 IBM watsonx.ai、Meta Llama 2 和 OpenAI Whisper 的使用。 - 通过实践课堂，实施功能性会议助理，展示生成式 AI 的商业应用。 5. 使用生成式 AI 和 RAG 总结私人数据： - 了解如何构建应用程序，使用自己的数据进行总结和问题回答。 - 探索检索增强生成（RAG）概念，使用 LangChain 和 Llama 2 创建能够检索和总结私人文档的聊天机器人。 6. 具有 LLM 和 STT/TTS 的通用语言翻译器： - 学习构建实时语音翻译助理，集成大语言模型与 IBM Watson 语音库。 - 了解语音到文本（STT）、翻译文本和语言转换的工作流程，应用 Python、Flask、HTML、CSS 和 JavaScript 创建用户友好的基于 Web 的语音助理。 7. [加分项目] 构建 AI 职业教练： - 学习使用大型语言模型构建个性化的 AI 职业教练。 - 探索如何利用生成式 AI 为求职者提供简历反馈、职位匹配和面试准备指导，通过实践实现针对用户输入生成个性化的职业建议。 通过这门课程，您将掌握实际应用生成式 AI 的技能，为实现真实世界的 AI 应用做好准备。

课程大纲

Name:Image Captioning with Generative AI

Description:In this module, you will explore the fundamentals of generative AI and foundation models, understanding how they drive modern AI applications. You will gain hands-on experience with image captioning using the bootstrapping language image pretraining (BLIP) model and build interactive UIs with Gradio. The module guides you through using Hugging Face for accessing pretrained models and datasets. You’ll also learn to deploy your AI app using IBM Code Engine for scalable access.

Name:Create Your Own ChatGPT-Like Website

Description:In this module, you will learn how to build your own ChatGPT-like application using generative AI tools. As part of the project, you will work with Facebook’s BlenderBot model using Hugging Face’s Transformers library in Python. You’ll explore key components such as large language models (LLMs), prompt engineering, and user interface design. Practical readings and labs will guide you through integrating models through APIs and deploying your app. You’ll also gain hands-on experience with frameworks like Gradio and Hugging Face. By the end, you’ll be equipped to create and customize your own conversational AI web app.

Name:Create a Voice Assistant

Description:In this module, you will explore how to build a generative AI-powered voice assistant by combining OpenAI’s GPT-3 with IBM Watson’s speech-to-text and text-to-speech services. You will learn how to structure the application, apply containerization using Docker for consistent deployment, and implement a basic voice assistant that can understand spoken input and respond naturally through synthesized speech. Finally, you will learn to deploy the chatbot to a public server.

Name:Generative AI-Powered Meeting Assistant

Description:In this module, you will learn how to build a generative AI-powered meeting assistant that can transcribe, summarize, and answer questions based on meeting content. You will explore key technologies such as IBM watsonx.ai, Meta Llama 2, and OpenAI Whisper, and understand their roles in creating enterprise-ready AI solutions. Through hands-on labs, you will implement a functional meeting assistant that showcases real-world business applications of generative AI.

Name:Summarize Your Private Data with Generative AI and RAG

Description:In this module, you will learn how to build generative AI applications that summarize and answer questions using your own data. You will explore the concept of retrieval-augmented generation (RAG), understand how tools like LangChain and Llama 2 support this process, and apply these technologies to create a functional chatbot that retrieves and summarizes private documents. This hands-on experience will prepare you to implement secure, domain-specific AI assistants in enterprise settings.

Name:Babel Fish (Universal Language Translator) with LLM and STT TTS

Description:In this module, you will acquire the skills to build a real-time voice translator assistant using generative AI technologies. You will learn how to integrate large language models, such as Flan-UL2, with IBM Watson® Speech Libraries for Embed to convert spoken input into translated speech output. The application workflow includes converting speech-to-text (STT), translating the text using an LLM, and converting it back to speech (TTS) in a target language. You will also apply your knowledge of Python, Flask, HTML, CSS, and JavaScript to create a functional and user-friendly web-based voice assistant that supports multilingual communication in real time. To support your learning, this module also includes a course glossary to reinforce key generative AI terms and technologies. You will conclude with a course wrap-up that summarizes major concepts and prepares you to apply your new skills to real-world AI applications.

Name:[Bonus] Module 7: Build an AI Career Coach

Description:In this module, you will learn to build a personalized AI-powered career coach using large language models (LLMs). You will explore how generative AI can assist job seekers by providing resume feedback, job matching insights, and interview preparation guidance. Through hands-on practice, you will implement a job application assistant that uses user inputs and prompt engineering to generate tailored career advice. This module will also help you understand how to apply LLMs in practical, user-centric scenarios that support professional development and career advancement.

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