Learners completing this course will be able to differentiate regression and classification tasks, apply logistic regression models in R, preprocess raw datasets, evaluate models using confusion matrices, and optimize performance through ROC curves, AUC, and threshold adjustments. They will also gain hands-on experience with real-world applications in healthcare and finance, including diabetes prediction and credit risk assessment.
Logistic Regression with R: Build & Predict
您将学到什么
Differentiate regression vs classification and apply logistic models.
Preprocess datasets, evaluate with confusion matrices and ROC.
Apply logistic regression to healthcare and finance case studies.
您将获得的技能
要了解的详细信息
可分享的证书
添加到您的领英档案
最近已更新！
September 2025
作业
12 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
This module introduces the fundamentals of logistic regression with R, guiding learners through data preparation, feature scaling, model fitting, and coefficient interpretation. Learners will gain the skills to prepare raw data and build a strong base for classification modeling.
涵盖的内容
9个视频4个作业
This module focuses on applying logistic regression to real-world datasets such as diabetes data, enhancing model performance through dimension reduction, and evaluating advanced metrics including ROC and AUC. Learners will master techniques to optimize classification outcomes.
涵盖的内容
9个视频4个作业
This module explores financial applications of logistic regression, including credit risk modeling, loan approval prediction, and dataset management. Learners will develop practical skills to build predictive models for financial decision-making.
涵盖的内容
9个视频4个作业
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