Attribute

Detail

Format

Online (e-LMS)

Level

Advanced

Duration

6 Months

Certification

e-Certification + e-Marksheet

Tools

R, Python, TensorFlow, Keras, Docker, Kubernetes

About the R Language Course

R Language – Use in AI Course dives deep into R Language – Use In Ai.

Gain comprehensive expertise through our structured curriculum and hands-on approach.

Program Highlights

• Comprehensive coverage of R Language Use in AI from fundamentals to advanced applications

• Hands-on projects and real-world case studies in AI

• 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

• Practical experience with tools: R, Python, TensorFlow, Keras

• Career-oriented training for academic and professional growth in AI

Course Curriculum

Module 1: 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

Module 2: 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

Module 3: 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

Module 4: 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

Module 5: 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

Module 6: 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

Module 7: 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

Tools, Techniques, or Platforms Covered

R
Python
TensorFlow
Keras
Docker
Kubernetes

Real-World Applications

Apply R Language Use in AI skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical AI competencies
Solve industry-relevant problems using R Language Use in AI methodologies and tools
Contribute to open-source projects and collaborative research in AI
Prepare for competitive examinations, interviews, and professional certifications in AI

Who Should Attend & Prerequisites

Designed for Professionals.
Designed for Students.
Foundational knowledge of artificial intelligence and familiarity with core concepts recommended.
Mentorship by industry experts and NSTC faculty.

Prerequisites:

Frequently Asked Questions

1. What is the format of this R Language Use in AI course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of AI concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 6 Months. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to AI. Our mentors are industry experts and experienced professionals.

Enroll in R Language Use in AI today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI skills that matter.

Format	Online (e-LMS)

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!