Online/ e-LMS
Mentor Based
Advanced
4 weeks
About
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.
Aim
The course aims to explore the capabilities of the R language in artificial intelligence, equipping participants with the skills to leverage R’s statistical and machine learning capabilities for AI applications.
Program Objectives
- Proficiency in R for AI: Gain a thorough understanding of how to use R for AI projects.
- Data Science Skills: Acquire skills in data manipulation, visualization, and analysis using R.
- AI Solution Development: Develop the ability to implement comprehensive AI solutions using R.
Program Structure
-
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.
- Subsection 1.1.2: R Syntax and Data Structures
- Variables, operators, and basic data types in R.
- Vectors, matrices, data frames, and lists.
- Subsection 1.1.3: R Packages for AI
- Installing and using essential R packages:
tidyverse,caret,randomForest,ggplot2. - Overview of AI-focused libraries:
keras,tensorflow,xgboost.
- Installing and using essential R packages:
Section 1.2: Introduction to Artificial Intelligence and Machine Learning
- Subsection 1.2.1: Overview of AI and ML
- Definitions: Artificial Intelligence, Machine Learning, Deep Learning.
- Supervised vs. Unsupervised Learning.
- Subsection 1.2.2: R’s Role in AI
- Why R is widely used for data analysis and AI tasks.
- Key features of R that make it suitable for AI.
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().
- Subsection 2.1.2: Data Cleaning
- Handling missing values (
na.omit(),impute(), etc.). - Outlier detection and removal.
- Data type conversions and normalization.
- Handling missing values (
Section 2.2: Feature Engineering and Selection
- Subsection 2.2.1: Feature Transformation
- Creating new features through transformations (logarithmic, polynomial, etc.).
- Scaling features with standardization (
scale()), normalization, and Min-Max scaling.
- Subsection 2.2.2: Feature Selection and Dimensionality Reduction
- Techniques for feature selection: correlation matrix, mutual information, feature importance.
- PCA (Principal Component Analysis) for dimensionality reduction.
Section 2.3: Handling Imbalanced Data
- Subsection 2.3.1: Resampling Techniques
- Oversampling and undersampling techniques to balance classes.
- Synthetic data generation (SMOTE technique).
- Subsection 2.3.2: Evaluating Class Imbalance Models
- Metrics: Precision, Recall, F1-Score, ROC curve, AUC.
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.
- Subsection 3.1.2: Classification Models
- Building models: Logistic Regression, k-NN, Decision Trees, Random Forest.
- Hyperparameter tuning and model evaluation techniques.
Section 3.2: Unsupervised Learning in R
- Subsection 3.2.1: Clustering Techniques
- K-Means Clustering: Algorithm, implementation, and evaluation.
- Hierarchical Clustering and DBSCAN.
- Subsection 3.2.2: Dimensionality Reduction Techniques
- Applying PCA (Principal Component Analysis) to reduce features.
- T-SNE for data visualization and exploration.
Section 3.3: Advanced Machine Learning Models in R
- Subsection 3.3.1: Ensemble Learning
- Random Forests and Boosting algorithms (XGBoost, AdaBoost).
- Bagging and Boosting for improving model performance.
- Subsection 3.3.2: Support Vector Machines (SVM)
- Implementing SVM for classification tasks.
- Hyperparameter tuning and kernel methods.
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.
- Subsection 4.1.2: R Deep Learning Frameworks
- Overview of
kerasandtensorflowin R. - Setting up and using Keras in R for deep learning models.
- Overview of
Section 4.2: Building a Neural Network in R
- Subsection 4.2.1: Designing a Neural Network
- Understanding architecture: Input layer, hidden layers, and output layer.
- Implementing a basic feed-forward neural network using
keras.
- Subsection 4.2.2: Training and Evaluating Deep Learning Models
- Model fitting and training.
- Performance metrics: Accuracy, loss, confusion matrix.
Section 4.3: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Subsection 4.3.1: Implementing CNNs for Image Classification
- Overview of Convolutional Neural Networks.
- Example: Building a CNN for image classification in R.
- Subsection 4.3.2: Implementing RNNs for Time-Series and NLP
- Overview of Recurrent Neural Networks.
- Use cases: Time-series prediction and text generation.
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’strain(), andkerasmodels. - Loading models for prediction and inference.
- Saving models using
- Subsection 5.1.2: Deploying AI Models in R
- Introduction to RShiny for creating web applications with AI models.
- Deploying models with Plumber for API-based deployment.
Section 5.2: Model Optimization and Fine-tuning
- Subsection 5.2.1: Hyperparameter Tuning
- Grid search and random search techniques for hyperparameter optimization.
- Using
caretandmlrlibraries for tuning.
- Subsection 5.2.2: Cross-validation and Model Evaluation
- K-fold cross-validation and evaluation metrics.
- Handling overfitting and improving model generalization.
- Subsection 1.1.1: Installing R and RStudio
Participant’s Eligibility
- M.Tech, M.Sc, and MCA students in IT, Computer Science, and related fields.
- E0 & E1 level professionals in BFSI, IT services, consulting, and fintech looking to enhance their AI capabilities with R.
Program Outcomes
- Advanced R Skills: Mastery of R for advanced AI applications, including deep learning and NLP.
- Strategic AI Implementation: Ability to strategically implement AI solutions using R across various industries.
- Innovative Thinking: Enhanced capability to innovate and improve processes through AI technologies.
Fee Structure
Fee: INR 10,999 USD 165
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
- Professional Networking: Opportunities to connect with industry leaders and AI experts.
- Career Services: Support for job placement and career advancement in AI and data science fields.
- Industry Insights: Sessions with guest lecturers from the industry to provide real-world insights into AI applications.
Future Career Prospects
- Data Scientist: Specializing in AI and machine learning projects using R.
- AI Research Analyst: Developing new AI methodologies and technologies.
- Business Intelligence Developer: Implementing AI solutions in business contexts to drive decision-making.
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