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所在平台: Udemy |
课程主页: https://www.udemy.com/course/streamlit-bootcamp/
课程评论：没有评论
**课程名称：Streamlit Bootcamp** **课程概述：** 想为你的数据科学和机器学习项目快速、轻松地创建 Web 应用和仪表盘吗？本课程是你的不二之选！Streamlit 是一个开源的 Python 库，可以轻松创建和分享精美的、自定义的 Web 应用，用于机器学习和数据科学。这些应用可以用来分享分析结果、构建复杂的交互式体验，以及展示新的机器学习模型。你可以在几分钟内构建和部署强大的数据应用。 更重要的是，Streamlit 应用的开发和部署速度极快且灵活，通常能将应用开发时间从几天缩短到几小时。 **课程内容：** * Streamlit 中的不同输入类型 * 数据展示元素 * 布局和容器 * 如何在 Streamlit Web 应用中添加图片和视频 * 各种图表元素，如折线图、柱状图等 * 三个完整的机器学习与 Streamlit 项目： * 股票市场指数预测应用 * 卡路里消耗计算器应用 * 保险费预测应用 **学习收获：** 完成本课程后，你将能够： * 构建多个可以添加到你的数据科学和机器学习作品集中的应用程序。 * 掌握一项新的技能，为你的简历增添亮点。 * 快速使用 Streamlit 为你的数据科学和机器学习项目构建 Web 应用和仪表盘。
ARE YOU LOOKING A FAST AND EASY WAY TO CREATE WEB APPS AND DASHBOARDS FOR YOUR DATA SCIENCE AND MACHINE LEARNING PROJECTS THEN THIS IS THE PERFECT COURSE FOR YOU.Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps.On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.In this course you will learn:Different input types in streamlitData display elementsLayouts and ContainersHow to add images and videos to your Streamlit web appDifferent Chart elements like Line Chart, Bar Chart etc...3 Complete Projects using Machine Learning and Streamlit.Stock Market Index Prediction AppCalories Burned Calculator AppInsurance Premium Prediction AppAt the end of the course, you will have built several applications that you can include in your Data Science and Machine Learning portfolio. You will also have a new skill to add to your resume.After completing this course you will be able to quickly build web apps and dashboards for your Data Science and Machine Learning Projects using Streamlit.
