This module introduces unsupervised and probabilistic learning methods in R, focusing on clustering with K-Means and classification with Naive Bayes. Learners explore how to group unlabeled data into meaningful clusters and apply Bayes’ theorem to text and categorical data. Practical examples in R reinforce understanding of cluster visualization, probability computations, and classification accuracy.

This module explores advanced supervised learning techniques in R, including text mining with Naive Bayes and classification with Support Vector Machines. Learners analyze word frequency patterns, build document-term matrices, and develop spam detection models. They further master SVM concepts such as linear and nonlinear classification, the kernel trick, and RBF applications for optical character recognition (OCR).

This module focuses on techniques to simplify complex datasets and build predictive models with neural networks. Learners explore feature selection and extraction methods, apply Principal Component Analysis (PCA), and interpret eigenvalues and eigenvectors in R. The module concludes with neural network foundations, covering activation functions, topology, and weight adjustment for adaptive learning.

This module integrates advanced applications of machine learning in R, including time series forecasting, boosting methods, and market basket analysis. Learners develop forecasting models, apply ARIMA and Prophet for stock prediction, and implement gradient boosting to improve accuracy. The module concludes with association rule mining and an overview of emerging machine learning trends.