MP4 | Video: AVC 1920×1080 30fps | Audio: AAC 48KHz 2ch | Duration: 7h 18mGenre: eLearning | Language: English | Size: 1.26 GBLearn complete hands-on Regression Analysis for practical Statistical Modelling and Machine Learning in RLearn complete hands-on Regression Analysis for practical Statistical Modelling and Machine Learning in RLearnImplement and infer Ordinary Least Square (OLS) regression using RApply statistical- and machine-learning based regression models to deal with problems such as multicollinearityCarry out the variable selection and assess model accuracy using techniques such as cross-validationImplement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifierAboutWith 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 –FeaturesProvides in-depth training in everything you need to know to get started with practical R data scienceThe course will teach the student with a basic-level statistical knowledge to perform some of the most common advanced regression analysis-based techniquesEquip students to use R to perform different statistical and machine learning data analysis and visualization tasks