Course Outline

Module 1: Introduction to R

• Introduction to R
• Various libraries in R and importation of data
• Data cleaning and reading using R
• Working with variables, vectors, matrices, factors, data frames, lists, and arrays in R
• Learning different data types in R
• Learning about various models in R
• Case Study: Analyzing and Cleaning Sales Data from a Retail Store to Create a Summary Report

Module 2: Introduction to Machine Learning

• Introduction to Machine Learning
• Comparison of Supervised and Unsupervised Learning
• R libraries suitable for machine learning
• Linear and Logistic Regression using R
• Understanding robust models used in machine learning
• Case Study: Building and Evaluating a Predictive Model for Customer Churn Using Logistic Regression

Module 3: Data Mining in R

• K-Nearest Neighbour
• Decision Trees
• Logistic Regression
• Support Vector Machines
• Outlier Detection
• Model Evaluation
• Case Study: Using Decision Trees and Support Vector Machines to Identify Fraudulent Transactions in Financial Data

Module 4: Neural Networking using R

• Understanding Neural Networks
• Learning about Activation Functions, Hidden Layers, Hidden Units
• Training a Perceptron
• Important Parameters of Perceptron
• Limitations of a Single-Layer Perceptron
• Illustrating Multi-Layer Perceptron
• Back-propagation – Learning Algorithm
• Understanding Back-propagation – Using Neural Network Example in R
• Case Study: Developing a Neural Network Model to Predict Product Demand Based on Historical Sales Data

Module 5: Clustering Analysis in R

• K-means Clustering
• Hierarchical Clustering
• Density-Based Clustering
• Gaussian Clustering Model
• Case Study: Segmenting Customers Based on Purchase Behavior Using K-means and Hierarchical Clustering