Hello everyone and welcome to this new hands-on project on Scikit-Learn Library for solving machine learning classification problems. In this project, we will learn how to build and train classifier models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means.

Scikit-Learn For Machine Learning Classification Problems

位教师:Ryan Ahmed
访问权限由 Coursera Learning Team 提供
3,207 人已注册
您将学到什么
Evaluate several trained classification models performance using various Key Performance Indicators (KPIs)
Understand the theory and intuition behind XG-Boost classifier model
Train machine learning classifier models using Scikit-Learn library
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在 2 小时内学习、练习并应用岗位必备技能
- 接受行业专家的培训
- 获得解决实训工作任务的实践经验
- 使用最新的工具和技术来建立信心

关于此指导项目
分步进行学习
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
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Understand the Problem Statement
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Import Libraries and Datasets
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Practice Opportunity #1 [Optional]
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Perform Exploratory Data Analysis
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Create Training and Testing Datasets
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XG-Boost Algorithm Overview
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Train an XG-Boost Algorithm Using Scikit-Learn
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Evaluate Trained Model Performance
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Practice Opportunity #2 [Optional]
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Final Capstone Project
10个项目图片
位教师

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学习方式
基于技能的实践学习
通过完成与工作相关的任务来练习新技能。
专家指导
使用独特的并排界面,按照预先录制的专家视频操作。
无需下载或安装
在预配置的云工作空间中访问所需的工具和资源。
仅在台式计算机上可用
此指导项目专为具有可靠互联网连接的笔记本电脑或台式计算机而设计,而不是移动设备。
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已于 Nov 11, 2022审阅
Good. Instructor have explained it quite nicely in limited time.





