Aim

This course focuses on the use of the R programming language in the field of Artificial Intelligence (AI). Participants will learn how to apply R's powerful libraries and tools to build machine learning models, perform data analysis, and implement AI algorithms. The course will cover AI fundamentals, data manipulation, and the use of R in supervised and unsupervised learning, deep learning, and natural language processing.

Program Objectives

Understand the fundamentals of AI and machine learning using the R programming language.
Master R packages for data manipulation, visualization, and AI model building.
Learn how to implement supervised and unsupervised learning algorithms in R.
Develop skills in using R for deep learning, natural language processing, and other AI techniques.
Gain hands-on experience with real-world AI projects and datasets using R.

Program Structure

Module 1: Introduction to AI and R

Overview of AI: Concepts, algorithms, and real-world applications.
Getting started with R: Data types, structures, and packages.
Installing and using key R libraries for AI (e.g., caret, ggplot2, dplyr).

Module 2: Data Manipulation and Preprocessing in R

Handling missing data, data normalization, and feature engineering in R.
Exploring datasets: Importing, cleaning, and transforming data in R.
Data visualization techniques using ggplot2 for understanding patterns and distributions.

Module 3: Supervised Learning with R

Introduction to supervised learning: Regression and classification.
Building machine learning models in R: Linear regression, decision trees, random forests, etc.
Model evaluation and validation: Cross-validation, accuracy, precision, recall, and F1 score.

Module 4: Unsupervised Learning with R

Understanding unsupervised learning: Clustering and dimensionality reduction.
Implementing k-means clustering, hierarchical clustering, and PCA in R.
Evaluating clustering performance and visualizing results in R.

Module 5: Deep Learning with R

Introduction to deep learning: Neural networks and backpropagation.
Using Keras and TensorFlow in R for deep learning applications.
Building deep learning models: Image recognition, sentiment analysis, and more.

Module 6: Natural Language Processing (NLP) in R

Introduction to NLP: Text preprocessing, tokenization, and feature extraction.
Building text classification models in R using tm and textclean packages.
Sentiment analysis and topic modeling using R for NLP tasks.

Module 7: Model Deployment and Optimization in R

Deploying AI models in R: Introduction to shiny for building interactive applications.
Hyperparameter tuning and model optimization using caret and tuneR packages.
Evaluating and improving model performance: Feature selection and regularization techniques.

Final Project

Develop an AI solution using R for a real-world dataset (e.g., predictive modeling, classification, or NLP).
Apply machine learning and deep learning algorithms to solve a specific problem in the industry.
Example projects: Building a recommendation system, fraud detection, or customer segmentation model.

Participant Eligibility

Students and professionals with a basic understanding of programming and data analysis.
Those interested in using R for machine learning, AI, and data science projects.
Individuals looking to advance their career in AI and data science using R programming.

Program Outcomes

Proficiency in using R programming language for AI applications and machine learning.
Hands-on experience building machine learning and deep learning models in R.
Ability to implement natural language processing and unsupervised learning techniques in R.
Skills in deploying AI models and optimizing them for better performance.

Program Deliverables

Access to e-LMS: Full access to course materials, datasets, and resources.
Hands-on Project Work: Implement R models for various AI use cases and challenges.
Final Project: Complete a real-world AI project using R programming.
Certification: Certification awarded after successful completion of the course and final project.
e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

Data Scientist
AI Researcher
Machine Learning Engineer
Data Analyst
R Developer (AI Applications)

Job Opportunities

Data Science and AI Firms: Building AI solutions and predictive models using R.
Healthcare and Pharma Companies: Applying AI for drug discovery, diagnosis, and treatment prediction.
Technology Companies: Developing AI tools and products using R programming for machine learning.
Financial Institutions: Using R for predictive modeling, risk analysis, and fraud detection.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

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Artificial Intelligence(ai)

R Language – Use in AI Course

Course Overview

Aim

Program Objectives