R Language for AI

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.

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.

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.

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.

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.

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