Introduction to Machine Learning

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.

Data Preparation and Exploration

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.

Data Visualization

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.

Machine Learning Techniques

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

Principal Components and Factor Analysis

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

Cluster Analysis

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. 

Decision Tree & SVM

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.

Artificial Neural Networks

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.

Text Mining

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

Model Evaluation and Improvement

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.