Another exquisit release
Packt.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R-XQZT
Title: Regression Analysis for Statistics and Machine Learning in R
Publisher: Packt
Size: 1.3G (1362188158 B)
Files: 6F
Date: 11/28/2019
Course #: 9781838987862
Type: N/A
Published: 28 Nov 2019
Modified: N/A
URL: www.packtpub.com/programming/regression-analysis-for-statistics-and-machine-learning-in-r-video
Author: N/A
Duration: 7 hours 18 minutes
Skill: N/A
Exer/Code: [X]
Installation:
Unpack that shit, run that shit
Description:
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
modelsAll the code and supporting files for this course are
available at -