Machine Learning with R

Introduction

Do you want to see examples of apply machine learning to real-world data using R?

• applying statistical modeling methods to open data
• demonstrating applied machine learning on on a medium-sized dataset
• introducing machine learning concepts and algorithms
• interpreting metrics and results with various plots

What you’ll learn

• learn how to use metrics to compare models
• data transformations
• validate and comparing models
• understand how to apply statistical methods
• model selection
• non-parametric models and techniques
• cross-validation

Topics covered

Statistical Modeling using Logistic Regression, CART, and Random Forests

Cross validation of Tuning Parameters

Data Transformations and Polynomial Terms

General Additive Models and Splines

Ridge and Lasso Regression Models

Model selection with Ensemble Methods including Gradient Boosting Trees and Random Forests