Machine learning in R

Training agenda

Part one: Let’s get it started!

• What’s Machine Learning?
• Algorithm distinction
• Machine Learning workflow

Part two: The necessary foundation

• Training data vs.Test data
• Exploratory Data Analysis
• Feature selection/extraction
• Overfitting
• Model validation

Part three: Overview of algorithms

• Statistical modeling recap (linear regression, logistic regression)
• Decision trees
• SVM
• Ensemble methods
  • Random Forest
  • Boosting

Part four: In search of best solution…

• Dimensionality reduction techniques
• Regularization
• Hyperparameter tuning
• Black-box model interpretability