Machine Learning with R

This program focuses on applying machine learning techniques using the R programming language. It is designed for data analysts and researchers who prefer R for statistical computing and data modeling. Participants will learn how to build, evaluate, and optimize machine learning models using R-based tools and libraries.

Introduction to Machine Learning with R

• Overview of machine learning concepts

• Why use R for data science and ML

• ML workflow in R environment

• Real-world applications

• R ecosystem for analytics

Data Handling in R

• Data import and manipulation

• Cleaning and preprocessing data

• Handling missing values

• Data visualization using R

• Exploratory data analysis

Supervised Learning in R

• Regression models in R

• Classification techniques

• Training and testing models

• Model evaluation methods

• Improving prediction accuracy

Unsupervised Learning in R

• Clustering techniques in R

• Principal Component Analysis (PCA)

• Pattern discovery in datasets

• Data grouping strategies

• Real-world use cases

Model Evaluation & Deployment

• Performance evaluation metrics

• Model tuning and optimization

• Validating machine learning models

• Exporting and sharing models

• Practical ML implementation workflows