Machine Learning in R: Exploring Foundational Datasets

Mastering machine learning requires more than just knowing algorithms; it requires understanding how to explore, clean, and model real-world data. Working with classic datasets is the most effective way to build this intuition and gain practical confidence. In this written course, you will learn how to perform predictive analytics in R from scratch. By analyzing foundational datasets such as the Titanic and Adult Census Income, you will transform raw data into actionable predictive insights. You will learn how to: Understand core machine learning concepts and foundational terminology in R; Explore and preprocess classic datasets like Titanic and Adult Census Income; Apply modern tidyverse and tidymodels workflows for clean, reproducible code; Build predictive classification models to solve real-world problems; Evaluate model performance using key metrics like accuracy and ROC curves; Practice writing robust R code to handle missing data and feature engineering. The course begins with essential definitions and data exploration concepts before guiding you step-by-step through data preparation, model training, and evaluation. You will progress through structured text explanations and practical code snippets designed for immediate application. This course is designed for beginners in data science and R programming, with no advanced mathematical or programming prerequisites required. Start your journey into R machine learning today.