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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-ai-app-dev/
课程评论：没有评论
课程名称：生成式人工智能应用设计与开发 课程概述：您是否对学习生成式人工智能感到兴趣，但又被人工智能和机器学习的复杂性所畏惧？如果是这样，那么这门课程将非常适合您！课程的结构是基于我自身学习生成式人工智能技术的经历，旨在简化学习过程，提升学习的可及性。这门课程特别设计为面向没有人工智能或机器学习背景的IT专业人士、开发者和架构师，帮助您快速掌握生成式人工智能的基础知识。 课程内容包括： 1. 生成式人工智能基础：深入核心概念，学习如何使用大型语言模型（LLM），如Google Gemini、Anthropic Claude、OpenAI GPT及多个开源/Hugging Face LLM。 2. 生成式人工智能应用构建：探索创建生成式AI应用的实用技术，包括提示技术、推理控制、上下文学习、RAG模式等。 3. 最新工具与框架：实践使用前沿工具如LangChain、Streamlit、Hugging Face及流行的向量数据库（例如Pinecone和ChromaDB）。 4. 多个LLM的应用：课程鼓励学生使用多个模型进行练习，以学习其行为细微差别。 5. 学习强化：每一组概念课后设置练习、项目和测验，以巩固理解和强化学习内容。 6. 利用Hugging Face平台：掌握其工具、库和社区资源，充分利用预训练模型并构建自定义应用。 7. 高级技术：深入研究嵌入、搜索算法、模型架构和微调等主题，提升您的AI能力。 8. 实际项目：通过动手项目应用所学知识，如构建电影推荐系统和创意写作工作台。 课程特点： - 23+小时的视频内容 - 动手项目及编码练习 - 真实案例 - 学习强化测验 - GitHub解决方案库 - 基于网络的课程指南 课程适合对象： - IT专业人士、应用开发者和架构师，欲将生成式AI整合到应用中； - 准备生成式AI相关职位面试的学生或专业人士； - 对人工智能/机器学习没有任何背景，但希望在快速发展的技术领域保持竞争力的人； - 希望学习如何构建能解决现实商业问题的智能系统的人。 选择此课程的理由： - 课程以讲师Raj的学习经历为基础，致力于帮助没有AI/ML背景的人快速掌握生成式AI。 - 不需要AI/ML背景：课程专为非专业人士和初学者设计。 - 动手学习：参与实际的项目和编码练习，使AI概念变得生动。 - 专家指导：由拥有超过20年行业经验的IT顾问Rajeev Sakhuja授课。 - 完整的课程大纲：拥有超过18小时的视频课程、测验和练习，以及支持学习过程的网络课程指南。 不适合的对象： - 寻求深入了解生成式AI模型内部机制的人； - 希望理解模型背后数学的人； - 对数据科学职位感兴趣的IT专业人士。
Are you interested in learning generative AI, but feel intimidated by the complexities of AI and ML? If your answer is YES, then this course is for you! I structured this course based on my own journey learning generative AI technology. Having faced the challenges firsthand, I've designed it to make the learning process easier and more accessible. This course is tailored specifically for those without an AI or ML background, helping you quickly get up to speed with generative AI.Designed specifically for IT professionals, developers, and architects with no prior AI/ML background, this course will empower you to build intelligent, innovative applications using Large Language Models (LLM). You'll gain practical, hands-on experience in applying cutting-edge generative AI technologies without the steep learning curve of mastering complex algorithms or mathematical theories.Here is an overview of course structure & coverage:Generative AI Foundations: Dive into the core concepts of Large Language Models (LLM), and learn how to work with powerful models like Google Gemini, Anthropic Claude, OpenAI GPT, and multiple open-source/Hugging Face LLMs.Building Generative AI Applications: Discover practical techniques for creating generative AI applications, including prompting techniques, inference control, in-context learning, RAG patterns (naive and advanced), agentic RAG, vector databases & much more.Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone and ChromaDB.Try out multiple LLM: Course doesn't depend on a single LLM for hands-on exercises, rather learners are encouraged to use multiple models for exercises so that they learn the nuances of their behavior.Learning Reinforcement: After each set of conceptual lessons, students are given exercises, projects, and quizzes to solidify their understanding and reinforce the material covered in previous lessons.Harnessing the Power of Hugging Face: Master the Hugging Face platform, including its tools, libraries, and community resources, to effectively utilize pre-trained models and build custom applications.Advanced Techniques: Delve into advanced topics like embeddings, search algorithms, model architecture, and fine-tunings to enhance your AI capabilities.Real-World Projects: Apply your knowledge through hands-on projects, such as building a movie recommendation engine and a creative writing workbench.Course Features23+ Hours of Video ContentHands-On Projects and Coding ExercisesReal-World ExamplesQuizzes for Learning ReinforcementGitHub Repository with SolutionsWeb-Based Course GuideBy the end of this course, you'll be well-equipped to leverage Generative AI for a wide range of applications, from natural language processing to content generation and beyond.Recent course updates:* May 2025 Model Context Protocol (MCP)* May 2025 Support for Python UV based environment* Feb 2025 Fine-tuning of LLM* Mar 2025 Added multiple new sections to the course guideWho Is This Course For?This course is perfect for:IT professionals, application developers, and architects looking to integrate generative AI into their applications.Students or professional preparing for interviews for the roles related to generative AIThose with no prior experience in AI/ML who want to stay competitive in today's rapidly evolving tech landscape.Anyone interested in learning how to build intelligent systems that solve real-world business problems using AI.Why Choose This Course?Raj structured this course based on his own experience in learning Generative AI technology. He applied his first hand knowledge of challenges faced in learning generative to create a structured course aimed at making it simple for anyone without AI/ML background to be able to get up to speed with generative ai fast.No AI/ML Background Needed: This course is designed for non-experts and beginners in AI/ML.Hands-On Learning: Engage in practical, real-world projects and coding exercises that bring AI concepts to life.Expert Guidance: Learn from Rajeev Sakhuja, a seasoned IT consultant with over 20 years of industry experience.Comprehensive Curriculum: Over 18 hours of video lessons, quizzes, and exercises, plus a web-based course guide to support you throughout your learning journey.Latest Tools and Frameworks: Gain practical experience with cutting-edge tools like LangChain, Streamlit, Hugging Face, and popular vector databases like Pinecone , FAISS, and ChromaDBWho should NOT take this course?Folks looking for deep dive into the internals of generative AI modelsLooking to gain understanding of mathematics behind the modelsIT professionals interested in DataSciences role
