Build practical machine learning skills in R by completing real-world projects with the caret package. Master Machine Learning Projects in R with Caret guides you through a structured workflow, from reading datasets and evaluating data quality to preparing data for clustering and unsupervised learning. You will learn how to detect and handle missing values, evaluate dataset attributes, apply correlation analysis, address data imbalance, choose appropriate imputation strategies, preprocess datasets, and implement clustering techniques to identify meaningful patterns.

Designed for students, professionals, and data enthusiasts, this course emphasises hands-on, project-based learning rather than theory alone. Each module builds on the previous one, helping you develop confidence in preparing reliable datasets, validating data quality, and applying essential preprocessing techniques before modelling. You will also gain practical experience in reproducing research results and streamlining machine learning workflows using R. What sets this course apart is its end-to-end focus on data preparation and clustering within a single machine learning project. By the end of the course, you will be able to structure machine learning projects, prepare high-quality datasets, implement clustering with the caret package, and interpret results with greater confidence for real-world data analysis.