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Regression Analysis for Statistics and Machine Learning in R [Video]

By Minerva Singh
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Video $180.99
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This book & 7000+ ebooks & video courses on 1000+ technologies
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50+ new titles added every month on new and emerging tech
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50+ new titles added every month on new and emerging tech
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50+ new titles added every month on new and emerging tech
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What do you get with a Packt Subscription?
This book & 6500+ ebooks & video courses on 1000+ technologies
60+ curated reading lists for various learning paths
50+ new titles added every month on new and emerging tech
Early Access to eBooks as they are being written
Personalised content suggestions
Customised display settings for better reading experience
50+ new titles added every month on new and emerging tech
Playlists, Notes and Bookmarks to easily manage your learning
Mobile App with offline access
What do you get with eBook?
Download this book in EPUB and PDF formats
Access this title in our online reader
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Online reader with customised display settings for better reading experience
What do you get with video?
Download this video in MP4 format
Access this title in our online reader
DRM FREE - Watch whenever, wherever and however you want
Online reader with customised display settings for better learning experience
What do you get with video?
Stream this video
Access this title in our online reader
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Online reader with customised display settings for better learning experience
What do you get with Audiobook?
Download a zip folder consisting of audio files (in MP3 Format) along with supplementary PDF
What do you get with Exam Trainer?
Flashcards, Mock exams, Exam Tips, Practice Questions
Access these resources with our interactive certification platform
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  1. Free Chapter
    Get Started with Practical Regression Analysis in R
About this video
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
Publication date:
November 2019
Publisher
Packt
Duration
7 hours 18 minutes
ISBN
9781838987862

About the Author
  • Minerva Singh

    Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.

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Regression Analysis for Statistics and Machine Learning in R [Video]
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