Regression Analysis for Statistics and Machine Learning in R [Video]
Regression Analysis for Statistics and Machine Learning in R [Video]
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 7h 18m | 1.28 GB
eLearning | Skill level: All Levels
Regression Analysis for Statistics and Machine Learning in R [Video]: Learn complete hands-on Regression Analysis for practical Statistical Modelling and Machine Learning in R
With so many R Statistics and Machine Learning courses around, why enroll for this?
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
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.
