Master advanced machine learning with R by learning how to build, evaluate, and interpret predictive models using a structured progression from statistical foundations to modern machine learning techniques. In this course, you will apply K-Means clustering, Naive Bayes classification, Support Vector Machines (SVM), Principal Component Analysis (PCA), neural network fundamentals, time series forecasting, gradient boosting, and market basket analysis through practical R programming examples.

You will learn how to cluster unlabeled data, classify text and categorical data, construct document-term matrices, apply kernel methods for accurate classification, reduce dimensionality with PCA, interpret principal components, design foundational neural networks, and develop forecasting models using ARIMA and Prophet. You will also improve predictive performance with gradient boosting and uncover associations through market basket analysis while strengthening your ability to preprocess data, select appropriate algorithms, and interpret model results. Designed for data analysts, aspiring data scientists, and professionals seeking to expand their machine learning expertise with R, this course combines theory with hands-on implementation and real-world case studies. Its unique structure brings together unsupervised learning, supervised learning, dimensionality reduction, neural networks, forecasting, and association rule mining in one comprehensive learning experience. By the end of the course, you will be able to confidently apply advanced machine learning techniques in R to analyse data, build predictive models, and make data-driven decisions.