Regression Analysis for Statistics & Machine Learning in R

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R

Created by: Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

What Will I Learn?

• Implement and infer Ordinary Least Square (OLS) regression using R
• Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
• Carry out variable selection and assess model accuracy using techniques like cross-validation
• Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
• Build machine learning based regression models and test their robustness in R
• Learn when and how machine learning models should be applied
• Compare different different machine learning algorithms for regression modelling

Requirements

• Should have prior experience of working with R and RStudio
• Should have basic knowledge of statistics
• Should have prior experience of using simple linear regression modelling
• Should have interest in building on the previous concepts to learn which regression models are applicable under different circumstances
• Should have an interest in learning the machine learning based regression models in R

Target audience

• People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
• People with basic knowledge of R based statistical modelling
• People with knowledge of linear regression modelling
• People wanting to extend their knowledge of regression modelling for solving real world problems.
• People wanting to learn how to apply machine learning based regression models using R
• Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
• Academic researchers seeking to learn new techniques for data analysis
• Business data analysts who wish to use regression modelling for predictive analysis