Machine Learning Projects in R with Caret

Overview

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.

Syllabus

Getting Started with the Machine Learning Project

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.

Data Preparation and Clustering

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.