Overview

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Build a strong foundation in machine learning with R by combining statistical theory with practical implementation. In Master Machine Learning with R: Build, Analyze & Predict, you will learn how machine learning works, explore the differences between supervised and unsupervised learning, and develop essential R programming skills for data manipulation and preparation. As you progress, you will strengthen your understanding of statistical concepts, including regression, correlation, probability distributions, hypothesis testing, and model evaluation before applying these principles to predictive modelling. The course then guides you through core machine learning algorithms in R, including regression, classification, K-Nearest Neighbours (KNN), decision trees, random forests, and boosting. Along the way, you will learn how to interpret statistical outputs, avoid common data analysis mistakes, and improve model performance using ensemble learning techniques. Designed for students, aspiring data professionals, and anyone interested in data science with R, this course provides a structured, step-by-step learning experience that connects statistical foundations with practical machine learning applications. By the end of the course, you will be able to prepare datasets, analyse data, evaluate statistical models, implement machine learning algorithms in R, and make informed, data-driven predictions with greater confidence.

Syllabus

Getting Started with R and Machine Learning

This module introduces the foundations of Machine Learning and the R programming environment. Learners will explore the key concepts of supervised and unsupervised learning, regression versus classification, and the practical steps to apply machine learning to real-world problems. In addition, the module covers essential R programming skills for data manipulation, vector operations, and dataset preparation, ensuring a strong foundation for statistical and machine learning tasks.

Fundamentals of Statistics in R

This module covers statistical concepts essential for building and interpreting machine learning models. Learners will review core measures such as variance, correlation, R-squared, and standard error while identifying common statistical mistakes. The module also extends to advanced topics including linear regression, statistical assumptions, and interpretation of outputs, equipping learners with the ability to analyze data with confidence.

Probability Distributions and Hypothesis Testing

This module focuses on probability distributions and hypothesis testing, both critical to statistical inference. Learners will examine discrete and continuous probability distributions, variance-covariance structures, and hypothesis rejection criteria. The module also introduces classical distributions such as t, chi-square, and Poisson, along with visualization techniques for testing data assumptions and interpreting results.

Core Machine Learning Algorithms

This module introduces core machine learning algorithms, focusing on regression, classification, decision trees, and ensemble methods. Learners will explore K-Nearest Neighbors (KNN), generalized regression models, decision tree classifiers, and the use of pruning to improve performance. The module concludes with ensemble learning techniques, including random forests and boosting, for building powerful predictive models.