Regression Analysis for Statistics & Machine Learning in R

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Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R.

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64 on-demand videos & exercises
Level: Intermediate
7hrs 18mins
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What to know about this course

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 -

Who's this course 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'll 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.

Key Features

  • Provided practical in depth 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.

Course Curriculum

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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