Regression Analysis for Statistics and Machine Learning in R | Packt

Regression Analysis for Statistics and Machine Learning in R | Packt
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Genre: eLearning

Implement and infer Ordinary Least Square (OLS) regression using R
Apply statistical- and machine-learning based regression models to deal with problems such as multicollinearity
Carry out the variable selection and assess model accuracy using techniques such as cross-validation
Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier
With so many R Statistics and Machine Learning courses around, why enroll for this?

Regression analysis is one of the central aspects of both statistical- and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical, hands-on way. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to machine learning-based regression models.

Become a Regression Analysis Expert and Harness the Power of R for Your Analysis

• Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R

• Carry out data cleaning and data visualization using R

• Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.

• Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression

• Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.

• Evaluate the regression model accuracy

• Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.

• Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.

• Work with tree-based machine learning models

All the code and supporting files for this course are available at –

Provides in-depth training in everything you need to know to get started with practical R data science
The course will teach the student with a basic-level statistical knowledge to perform some of the most common advanced regression analysis-based techniques
Equip students to use R to perform different statistical and machine learning data analysis and visualization tasks

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