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.

What you will learn?

1. 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.

   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.

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   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.

   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.

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   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.

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   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 keras and tensorflow in R.
     • Setting up and using Keras in R for deep learning models.

   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.

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   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.
   • 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 caret and mlr libraries for tuning.
   • Subsection 5.2.2: Cross-validation and Model Evaluation
     • K-fold cross-validation and evaluation metrics.
     • Handling overfitting and improving model generalization.