R Language for AI
R Language for AI is a advanced-level, 4 weeks online course by NSTC. Master key concepts and practical skills in Science & Technology through hands-on projects, real-world case studies, and expert mentorship.
- ModeOnline (e-LMS)
- LevelAdvanced
- Duration4 weeks
- Fee₹5,499
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | R RStudio |
About the R Language for AI 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.
It covers the integration of R in data science, machine learning, deep learning, and natural language processing, providing practical skills and deep insights into R's use in AI-driven projects.
Program Highlights
• Comprehensive coverage of R Language for AI from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Science & Technology
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Science & Technology
• Career-oriented training for academic and professional growth in Science & Technology
Course Curriculum
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.
