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Machine Learning Projects in R with Caret

本课程是 AI Machine Learning with R & Python Projects 专项课程的一部分

位教师：EDUCBA

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Prepare datasets, handle missing values, and apply imputation.
Perform correlation analysis and manage data imbalance.
Implement clustering with caret and validate ML workflows.

您将获得的技能

您将学习的工具

R Programming

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

6 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

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Machine Learning with R & Python Projects 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

By the end of this course, learners will be able to prepare datasets, detect and handle missing values, apply imputation strategies, perform correlation analysis, address data imbalance, and implement clustering using the caret package in R. Participants will also gain hands-on experience in reproducing research results, validating data quality, and streamlining machine learning workflows.

This course is designed for students, professionals, and data enthusiasts who want to strengthen their applied machine learning skills in R. Unlike typical theory-driven courses, it emphasizes project-based learning, walking learners step by step through a complete workflow — from reading datasets to advanced preprocessing and clustering. What makes this course unique is its focus on real-world problem solving, integrating missing data handling, preprocessing, and unsupervised learning into a single, cohesive framework. Learners will acquire not only technical skills but also the confidence to structure, execute, and interpret machine learning projects effectively.

单元详情

This module introduces learners to the machine learning project framework using the caret package in R. It emphasizes understanding the project scope, reading datasets, and addressing fundamental data quality challenges such as missing values and attribute checks. Learners will build a solid foundation for effective data preprocessing and ensure readiness for advanced modeling stages.

涵盖的内容

5个视频3个作业

This module focuses on advanced data preparation techniques and clustering methods. Learners will explore correlation analysis, address data imbalance, select imputation strategies, preprocess imputed datasets, and implement clustering algorithms. By the end, learners will be able to prepare datasets for modeling and uncover meaningful patterns through unsupervised learning.