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Regression Analysis for Statistics & Machine Learning in R
Regression Analysis for Statistics & Machine Learning in R

Regression Analysis for Statistics & Machine Learning in R: Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R

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Profile Icon Minerva Singh
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Video Nov 2019 7hrs 18mins 1st Edition
Video
₱579.99 ₱9236.99
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Free Trial
Arrow left icon
Profile Icon Minerva Singh
Arrow right icon
Free Trial
Video Nov 2019 7hrs 18mins 1st Edition
Video
₱579.99 ₱9236.99
Subscription
Free Trial
Video
₱579.99 ₱9236.99
Subscription
Free Trial

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Key benefits

  • Provided practical indepth training for you to get started with R
  • Perform some of the most common advanced regression analysis-based techniques
  • Use R to perform different statistical and machine learning data analysis and visualization tasks

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 Statistics and Machine Learning based analysis. This course will teach you Regression analysis for both Statistical data analysis and ML in R. 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 ML 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 ML models All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R

Who is this book for?

This course is for students interested in getting started with data science applications in the RStudio environment. Students wishing to learn to implement statistical modeling techniques for regression analysis on real data. Anyone with prior exposure to R who wants to get started with practical data science.

What you will learn

  • Implement and infer Ordinary Least Square (OLS) regression using R
  • Apply statistical and ML 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 28, 2019
Length: 7hrs 18mins
Edition : 1st
Language : English
ISBN-13 : 9781838987862
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Languages :
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Product Details

Publication date : Nov 28, 2019
Length: 7hrs 18mins
Edition : 1st
Language : English
ISBN-13 : 9781838987862
Category :
Languages :
Tools :

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Table of Contents

7 Chapters
Get Started with Practical Regression Analysis in R Chevron down icon Chevron up icon
Ordinary Least Square Regression Modelling Chevron down icon Chevron up icon
Deal with Multicollinearity in OLS Regression Models Chevron down icon Chevron up icon
Variable & Model Selection Chevron down icon Chevron up icon
Dealing with Other Violations of the OLS Regression Models Chevron down icon Chevron up icon
Generalized Linear Models (GLMs) Chevron down icon Chevron up icon
Working with Non-Parametric and Non-Linear Data Chevron down icon Chevron up icon
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