In Module 2, you will learn the basics of Classification and understand the working of the kNN classifier.

To effectively learn Naive Bayes classification, this module will cover both the theoretical foundations and the practical implementation in R.

In order to understand the working of the Decision Tree as a classifier , we will need to grasp how this algorithm makes decisions and classifies new data points based on patterns it learned from training data.

This module focused on "Using Artificial Neural Networks (ANNs) as a classifier" aims to provide a comprehensive understanding of how these powerful, biologically inspired models can be applied to categorize data.

This module, "Classification using Support Vector Machines (SVMs)", will equip you with a deep understanding of this powerful machine learning algorithm and its application in classifying data.

This module on "Clustering" aims to introduce you to the powerful world of unsupervised learning, where the goal is to discover inherent groupings within unlabeled data.

Mining frequent item sets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.