Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
R Language - Use in AI is a structured 8-week program that introduces R programming to M.Tech, M.Sc, and MCA students, as well as professionals in various tech industries. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
R Language - Use in AI is a structured 4-week program that introduces R programming to M.Tech, M.Sc, and MCA students, as well as professionals in various tech industries.
📋 Course Objectives
1. Put Artificial Intelligence techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A demonstrable Artificial Intelligence project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
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.
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() .
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.
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.
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.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
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