Machine Learning using R

Course summary

This course provides a comprehensive introduction to machine learning using the R programming language. Learners explore foundational concepts such as supervised and unsupervised learning, data preparation, visualization, modelling techniques, and model evaluation. Through structured modules, the course equips learners with the essential skills to prepare datasets, apply ML algorithms, perform statistical and exploratory analysis, and leverage R packages to build, interpret, and improve machine-learning models.

Learning outcome

By the end of the course, learner will be able to:

  • Distinguish machine learning from related fields such as statistical learning, AI, and data mining.
  • Prepare, explore, and visualize datasets in R using appropriate packages and tools.
  • Apply core machine-learning techniques including regression, decision trees, clustering, SVM, PCA, and neural networks.
  • Conduct text mining and extract insights from unstructured data.
  • Evaluate, tune, and improve ML models using hyperparameter optimisation and R's caret package.
  • Select appropriate ML algorithms based on data characteristics and problem requirements.

Modules

The module gives an overview of machine learning and how it is different from statistical learning, artificial intelligence, and data mining. The module covers the different types of machine learning and its algorithms.

The module gives an introduction to artificial intelligence, machine learning, and IoT. It also gives an understanding of how to recognise and create datasets in R, explaining the structure, types, and data input tools.

The module gives an introduction to data and how it has evolved. The module covers the concept of the R package, its features, installation, and loading.

This module explains regression, its types and methodologies, and how to determine the relationship between two variables.

The module deals with methods of data analysis, principal data analysis, and exploratory factor analysis using examples.

The module gives an understanding of cluster analysis, methods of clustering, and the steps involved in its analysis. Calculating distances is explained with the help of examples.

The module explains decision trees, their types, how and when they are used, and gives a glimpse of the various packages offered by R software.

The module explains artificial neural networks, their types, and differences. also deals with the types of variables in neural networks and the commands to create them.

The module provides an overview of text mining and its related concepts, along with examples.

The module gives an understanding of when a model performance is used. The module explains the caret package, hyper parameters along with their architecture, hyperparameter optimization and its methods.