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.
Machine Learning Projects in R with Caret
Machine Learning Projects in R with Caret
This course is part of AI Machine Learning with R & Python Projects Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Prepare datasets, handle missing values, and apply imputation.
Perform correlation analysis and manage data imbalance.
Implement clustering with caret and validate ML workflows.
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
October 2025
Assessments
6 assignments
Taught in English
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This course is part of the AI Machine Learning with R & Python Projects Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 2 modules in this course
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.
What's included
5 videos3 assignments1 plugin
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.
What's included
5 videos3 assignments
Earn a career certificate
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