Overview

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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.

Syllabus

Clustering and Bayesian Models

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.

Advanced Supervised Learning

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).

Dimensionality Reduction and Neural Networks

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.

Advanced Applications in ML

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.