📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of R Language for AI, from foundations to a certified capstone project.
Outline
Subsection 1.1.1: Installing R and RStudio Overview of R language and the RStudio IDE. • Setting up R for AI development.
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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() .
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Subsection 3.1.1: Regression Models Building and evaluating Linear Regression, Ridge, and Lasso models. • Implementing Polynomial Regression for non-linear relationships.
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Subsection 4.1.1: Overview of Neural Networks Structure of neural networks: Layers, neurons, activation functions. • How deep learning differs from traditional machine learning.
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Subsection 5.1.1: Saving and Exporting Models Saving models using saveRDS() , caret ’s train() , and keras models. • Loading models for prediction and inference.
Earn government-registered certification in R Language for AI
e-Certificate and e-Marksheet issued on successful completion.
