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所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-machine-learning-web-application-with-streamlit/
课程评论：没有评论
**课程名称：** 使用 Streamlit 构建机器学习 Web 应用 **课程概述：** 本课程专为 Python 数据科学家设计，旨在帮助您将 Jupyter Notebook 中开发的机器学习算法转化为可分享或商业化的 Web 应用。即使您刚接触数据科学，也终将需要为您的模型构建应用。Streamlit 是解决此问题的理想工具。作为一款开源的 Python 库，Streamlit 能够让您轻松快捷地创建基于机器学习和数据科学的 Web 应用，且无需深入了解 HTML、CSS、JavaScript 等 Web 编程概念。通过几行代码，您即可构建强大的机器学习 Web 应用。 **课程内容：** 1. **Streamlit 入门介绍** 2. **Streamlit 安装与配置** 3. **洗钱概念讲解（结合案例演示）** 4. **从零开始创建机器学习 Web 应用** 5. **机器学习算法简介（如决策树、随机森林）** 6. **机器学习模型性能度量简述（如混淆矩阵、ROC 曲线、精确率-召回率曲线）** 通过本课程，您将获得宝贵的实践知识和技能。期待在课程中见到您！
So you are a data scientist who has created great machine learning algorithms in python notebook. Now, you are thinking of developing an application based on that algorithm that can be shared with others or you want to capitalize by selling that application.Even if you are just starting out in data science, you will reach a point, where you will look to build applications around your model.Streamlit is the answer to your question, it is an open-source Python library that makes it very easy to create a web application based on machine learning and data for data science. It is easy to learn, you don't need to understand all the concepts in web programming like HTML, CSS, javascript. Hence, you can build powerful machine learning web apps in a few lines of code at a great speed.In this course we will cover the following:1. Introduction to Streamlit2. Installation of Streamlit3. Concept of Money Laundering4. Creating Machine Learning Web App from Scratch5. Brief description of Machine Learning algorithms like Decision Trees and Random Forests6. Short Introduction to performance measure of Machine Learning Models like Confusion Matrix, ROC Curve, Precision and Recall CurveWe will gain a lot of useful knowledge from this course. I hope to see you in this course.
