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所在平台: Udemy |
课程主页: https://www.udemy.com/course/xgboost-python-r/
课程评论：没有评论
课程名称：10天：提示工程、生成性人工智能与数据科学 课程概述：欢迎参加“10天提示工程、生成性人工智能和数据科学”课程。本课程由Diogo讲授，旨在快速带您从基础知识学习到高级主题。在14小时30分钟的公开视频内容中，我们将通过现场课程、动手实验室和真实项目来实践提示工程、生成性人工智能和数据科学。您还将获得终身更新，确保学习内容保持新鲜。您将建立一个项目组合，涵盖如下主题： - **提示工程基础**：了解变换器、注意机制以及如何构建最佳性能的提示。 - **生成AI工作流**：掌握Google Colab、Jupyter Notebook和LM Studio等工具，学习如何微调系统消息和模型参数。 - **OpenAI API用于文本和图像**：将OpenAI API集成到Python项目中，探索改善文本生成的参数，并了解图像生成（即将推出）。 - **使用XGBoost和随机森林进行机器学习**：探索高级机器学习主题，包括参数调优、SHAP值和顾客满意度建模的实际方法。 - **使用CrewAI构建AI代理**：深入了解下一波AI自动化（预计2025年第一季度推出）。 **课程安排**： - **介绍**：认识您的讲师，下载课程材料，设置环境（Google Colab、Jupyter Notebook、RStudio）。预览我们将要进行的核心项目。 - **第1天 - 提示工程基础**：学习变换器、注意力机制和思维链提示，使用LM Studio进行显式指令、单次与少次技术的实验。 - **第2天 - 系统消息与LLM参数**：学习分词、系统消息和参数调优。 - **第3天 - 改进LLM推理的提示工程**：探索提升LLM推理的有效方法，以及如何克服LLM幻觉。 - **第4天 - 推理LLM**：了解推理在LLM中的工作原理（2025年第一季度）。 - **第5天 - OpenAI API用于文本生成**：将OpenAI API集成到Python中，调整生成文本的温度等。 - **第6天 - 课程项目：OpenAI API**：创建“猜拳”AI，测试不同策略和参数。 - **第7天 - OpenAI API用于图像生成**：通过链接和编码，向多模态LLM添加图像。 - **第8天 - 随机森林用于顾客满意度**：一个关于获取顾客满意度建议的完整项目。 - **第9天 - XGBoost**：深入了解Python和R中的XGBoost，进行数据处理和模型解读。 - **第10天 - CrewAI的AI代理**：学习如何构建自动化任务的AI代理（预计2025年第二季度）。 **为什么立即报名？** - **终身更新**：您将自动获得未来的课程模块，包括2025年计划的高级部分。 - **实践项目**：将所学知识应用于真实场景（如猜拳AI和顾客满意度的XGBoost）。 - **结构化课程**：每一天的设计都基于前一天的内容，快速提升您的学习与进展。 - **社区与反馈**：参与讨论，获得直接反馈，影响新内容的更新。 准备好加速您的提示工程、生成AI和数据科学技能了吗？现在就报名，立即访问所有已发布的内容，包括未来模块。让我们开始共同构建AI的未来！
Welcome to the 10 Days of Prompt Engineering, Generative AI, and Data Science CourseGet hands-on with Prompt Engineering, Generative AI, and Data Science in just 10 days. I'm Diogo, and I've structured this course to take you from basics to advanced topics quickly. We'll cover live sessions, hands-on labs, and real-world projects-all in 14 hours and 30 minutes of published video content. You'll also receive lifetime updates so your learning never goes stale.You will build a portfolio of project on topics like:Prompt Engineering Fundamentals: Understand transformers, attention mechanisms, and how to structure prompts for optimal performance.Generative AI Workflows: Master tools like Google Colab, Jupyter Notebook, LM Studio, and learn how to fine-tune system messages and model parameters.OpenAI API for Text & Images: Integrate the OpenAI API into Python projects, explore parameters for better text generation, and tap into image generation (coming soon).Machine Learning with XGBoost & Random Forest: Explore advanced ML topics, including parameter tuning, SHAP values, and real-world approaches to customer satisfaction modeling.AI Agents with CrewAI: Dive into the next wave of AI automation (coming in Q1 2025).COURSE BREAKDOWNIntroductionMeet your instructor, download course materials, set up your environment (Google Colab, Jupyter Notebook, RStudio).Preview the core projects we'll tackle.Day 1 - Basics of Prompt EngineeringLearn about transformers, attention, and chain-of-thought prompting.Experiment with LM Studio to practice explicit instructions, one-shot, and few-shot techniques.Day 2 - System Messages & LLM ParametersTokenization, system messages, and parameter tuning.Break the system message (on purpose) to see how LLMs respond, then learn how to guide them back.Days 3 - Prompt Engineering for better reasoningProven ways to improve the reasoning in LLMs.Overcoming LLM HallucinationsDay 4 -Reasoning LLMs - Coming in Q1 2025How Reasoning Works in LLMsPrompt Injection for LLMs like the O1.A hot take on whether LLMs can reason or not.Day 5 - OpenAI API for Text GenerationIntegrate the OpenAI API in Python.Adjust temperature, handle few-shot learning, and refine your text generation workflow.Day 6 - CAPSTONE PROJECT: OpenAI APIBuild a "Rock-Paper-Scissors" AI.Create new strategies, test temperature parameters, and see how GPT adapts.Days 7 - OpenAI API for ImagesFee images via links and encoded to the Multimodal LLMAdd Web-browsing capabilities to the LLMDay 8 - Random Forest for Customer SatisfactionEnd-to-end project on gathering actionable insights on customer satisfaction.Guide on how to build a great chart.Day 9 - XGBoostDiscover XGBoost in both Python and R.Handle data processing, parameter tuning, cross-validation, and SHAP values for model interpretation.Day 10 - AI Agents with CrewAIComing in Q2 2025-learn to build AI agents that automate tasks and collaborate efficiently.WHY ENROLL NOW?Lifetime Updates: You get all future course modules automatically, including advanced sections scheduled for 2025.Practical Projects: Apply what you learn in real-world scenarios (Rock-Paper-Scissors AI, XGBoost for customer satisfaction).Structured Curriculum: Each day is designed to build on the previous one, speeding up your learning and progress.Community & Feedback: Engage in discussions, get direct feedback, and influence new content updates.Ready to accelerate your Prompt Engineering, Generative AI, and Data Science skills?Sign up now and gain immediate access to all published content, including the future modules. Let's start building the future of AI together!
