Importance of Machine Learning with R Technology

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Course Content Covered in Machine Learning with R Training Course

Machine Learning with R Course Contents

•             Machine Learning versus Statistical Modeling and Supervised versus Unsupervised Learning

•             Machine Learning Languages, Types, and Examples

•             Machine Learning versus Statistical Modeling

•             Supervised versus Unsupervised Learning

•             Supervised Learning Classification

•             Unsupervised Learning

Supervised Learning I

•             K-Nearest Neighbors

•             Decision Trees

•             Random Forests

•             Reliability of Random Forests

•             Advantages and Disadvantages of Decision Trees

Supervised Learning II

•             Regression Algorithms

•             Model Evaluation

•             Model Evaluation: Overfitting and Underfitting

•             Understanding Different Evaluation Models

Unsupervised Learning

•             K-Means Clustering notwithstanding Advantages and Disadvantages

•             Hierarchical Clustering notwithstanding Advantages and Disadvantages

•             Measuring the Distances Between Clusters - Single Linkage Clustering

•             Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering

•             Density-Based Clustering

Dimensionality Reduction and Collaborative Filtering

•             Dimensionality Reduction: Feature Extraction and Selection

•             Collaborative Filtering and Its Challenges

Supervised Learning – Classification

•             Analyzing the supposition of reviews: A logical examination in Classification