R Language for AI

Module 1: Introduction to R and AI Fundamentals Section 1.1: Getting Started with R

• Subsection 1.1.1: Installing R and RStudio Overview of R language and the RStudio IDE.
• Setting up R for AI development.

Module 2: Data Preprocessing and Feature Engineering Section 2.1: Data Collection and Cleaning in R

• Subsection 2.1.1: Importing and Exploring Data Importing datasets from CSV, Excel, databases, and web sources.
• Summary statistics and basic exploration using summary() , str() , head() .

Module 3: Building AI Models in R Section 3.1: Supervised Learning in R

• Subsection 3.1.1: Regression Models Building and evaluating Linear Regression, Ridge, and Lasso models.
• Implementing Polynomial Regression for non-linear relationships.

Module 4: Deep Learning with R Section 4.1: Introduction to Deep Learning

• Subsection 4.1.1: Overview of Neural Networks Structure of neural networks: Layers, neurons, activation functions.
• How deep learning differs from traditional machine learning.

Module 5: Model Deployment and Optimization Section 5.1: Model Deployment in R

• Subsection 5.1.1: Saving and Exporting Models Saving models using saveRDS() , caret ’s train() , and keras models.
• Loading models for prediction and inference.