Regression and Machine Learning for Education Sciences Using R (Paperback, 1)

A brief introduction to R and R Studio Part 1: Regression models: Foundation of machine learning Chapter 01: First thing first: Simple regression Chapter 02: Beyond simple: Multiple regression analysis Chapter 03: It takes two to tangle: Regression with interaction Chapter 04: Are we thinking correctly: Checking assumptions of regression model Chapter 05: I am not straight but robust: Curvilinear Robust and Quantile regression Chapter 06: Predicting the class probability: Logistic regression Part 2: Machine learning: Classification and predictive modeling Chapter 07: Introduction to machine learning Chapter 08. Machine learning algorithms and process Chapter 09. Let me regulate: Regularized Machine learning Chapter 10. Finding ways in the forest: Prediction with Random Forest Chapter 11. I can divide better: Classification with support vector machine Chapter 12. Work like a human brain: Artificial neural network Chapter 13. Desire to find causal relations: Bayesian network Chapter 14. We want to see the relationships: Multivariate data visualization