Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• 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 builds practical machine-learning skills in the language built by and for statisticians. You work in the tidyverse for fluent data manipulation and visualisation, then move into modelling — from classical statistical methods where R excels to machine learning with caret and tidymodels. The course emphasises the analytical strengths that make R a first choice in research and biostatistics: rigorous inference, clear diagnostics and publication-quality reporting with R Markdown. You finish able to take a dataset through the full workflow of analysis, modelling and communication in R. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches R for data science and machine learning — the tidyverse, statistical modelling, ML with caret and tidymodels, and reproducible visual reporting.
📋 Course Objectives
1. Manipulate and visualise data with the tidyverse.
2. Apply statistical modelling and inference in R.
3. Build ML models with caret and tidymodels.
4. Produce reproducible reports with R Markdown.
5. Communicate results with publication-quality visuals.
👥 Who Should Enroll?
• Researchers and statisticians using R
• Analysts in biostatistics, social science or finance
• Data scientists adding R to their toolkit
• Students specialising in statistical computing
🚀 Key Learning Outcomes
• Fluency in R for analysis and machine learning.
• A reproducible R analysis project.
• Strong statistical-reporting skills.
• 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
AI Fundamentals, Mathematics, and R Language Foundations
Develop a comprehensive understanding of AI concepts, including machine learning, deep learning, and neural networks, using R programming language • Analyze mathematical prerequisites for AI, including linear algebra, calculus, and probability, and apply them to R-based AI applications • Configure R environment for AI development, including installation of necessary packages, such as caret, dplyr, and tidyr, for data manipulation and modeling
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using R, including data ingestion, cleaning, transformation, and feature engineering, for AI model development • Evaluate and select appropriate data preprocessing techniques, such as handling missing values, data normalization, and feature scaling, using R packages like tidymodels • Implement data visualization techniques using R, including ggplot2 and shiny, to communicate insights and trends in data for AI applications
Model Architecture, Algorithm Design, and R Language Methods
Develop and implement various AI models, including linear regression, decision trees, random forests, and neural networks, using R packages like keras and tensorflow • Analyze and compare different algorithmic approaches, including supervised, unsupervised, and reinforcement learning, using R for AI model development • Optimize model hyperparameters using R, including grid search, random search, and Bayesian optimization, for improved AI model performance
Training, Hyperparameter Optimization, and Evaluation
Train AI models using R, including data splitting, model training, and evaluation, using metrics like accuracy, precision, and recall • Implement hyperparameter tuning techniques, including cross-validation and walk-forward optimization, using R packages like caret and dplyr • Evaluate AI model performance using R, including metrics like mean squared error, mean absolute error, and R-squared, for regression tasks
Deployment, MLOps, and Production Workflows
Deploy AI models using R, including model serving, API development, and containerization, using tools like Docker and Kubernetes • Design and implement MLOps pipelines using R, including model monitoring, logging, and versioning, for production-ready AI applications • Configure and manage AI model workflows using R, including data ingestion, model inference, and result visualization, for automated decision-making
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and address ethical concerns in AI development, including bias, fairness, and transparency, using R for data analysis and visualization • Develop and implement strategies for bias mitigation, including data preprocessing, feature engineering, and model selection, using R packages like tidymodels • Evaluate and communicate AI model explainability using R, including techniques like feature importance, partial dependence plots, and SHAP values
Industry Integration, Business Applications, and Case Studies
Develop and implement AI solutions for real-world business problems, including customer segmentation, demand forecasting, and recommender systems, using R • Analyze and evaluate AI applications in various industries, including healthcare, finance, and marketing, using R for data analysis and visualization • Design and implement AI-powered business intelligence dashboards using R, including data visualization, reporting, and decision-making
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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