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所在平台: Udemy |
课程主页: https://www.udemy.com/course/web-calculators-with-machine-learning-models-in-python/
课程评论：没有评论
**Coursera课程总结：使用Python机器学习模型构建Web计算器** 本课程专为希望通过开发Web应用程序展示机器学习模型来提升数据分析技能的数据爱好者、求职者、学生和专业人士设计。 **课程亮点：** * **从基础开始：** 即使是初学者也能轻松上手，学习如何使用Scikit-Learn训练和导出机器学习模型。 * **构建动态Web应用：** 学习加载模型并模拟后端，为Web应用程序赋予动态和交互性。 * **Streamlit入门：** 掌握使用Streamlit框架创建直观的Web计算器，简化数据应用程序开发。 * **部署应用：** 了解如何将应用程序部署到Streamlit Share，扩大受众范围。 * **模型解释（SHAP）：** 深入学习SHAP（Shapley Additive exPlanations），探索可视化技术解释模型预测。 * **高级技巧：** 模拟后端进程、处理基于变量平均值的动态默认值，并遵循DRY（Don't Repeat Yourself）等编码最佳实践。 * **健壮的Pipeline：** 构建用于预处理和建模的健壮Pipeline，动态处理输入值，提升表单的交互性。 **课程收益：** 完成本课程后，您将掌握开发和部署完整的机器学习Web应用程序的技能，极大地提升您的作品集和专业能力。 **学习工具：** * **Python** * **Scikit-Learn** (用于机器学习模型) * **Streamlit** (用于Web应用程序开发) * **SHAP** (用于模型解释) 立即加入，利用Streamlit和Scikit-Learn将您的数据科学项目转化为功能齐全的Web应用程序！
This comprehensive course is designed for data enthusiasts, job seekers, students, and professionals who want to take their data analysis skills to the next level by developing web applications that showcase their machine learning models.In this course, we start from the basics, ensuring that even beginners can follow along comfortably. You will learn how to train and export machine learning models using Scikit-Learn, one of the most popular libraries in the Python ecosystem. We will guide you through loading these models and simulating a backend to make your web applications dynamic and interactive.Our journey begins with an introduction to training, exporting, and simulating the backend of machine learning models. Next, you will learn how to create intuitive web calculators using Streamlit, a powerful framework that simplifies the development of data applications. We cover everything from basic setup to deploying your applications on Streamlit Share, making your work accessible to a broader audience.Then, we dive into SHAP (Shapley Additive exPlanations), where you'll explore various visualization techniques to explain your model's predictions. You'll learn how to simulate backend processes, handle dynamic default values based on variable averages, and follow coding best practices like DRY (Don't Repeat Yourself).In the final section, we focus on building robust pipelines for preprocessing and modeling. You will gain practical experience processing input values to make your forms more dynamic and simulating backend processes with pipelines. By the end of this course, you will have the skills to develop and deploy complete web applications that leverage machine learning models, giving a significant boost to your portfolio and professional capabilities.Join us and transform your data science projects into fully functional web applications with Streamlit and Scikit-Learn.
