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Regression Analysis for Statistics & Machine Learning in R

Link : Regression Analysis for Statistics & Machine Learning in R

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 manner. BESTSELLER

Regression Analysis for Statistics & Machine Learning in R
4.3 (359 ratings)
2,804 students enrolled
Created by Minerva Singh

What you'll learn
  • Implement and infer Ordinary Least Square (OLS) regression using R
  • Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
  • Carry out variable selection and assess model accuracy using techniques like cross-validation
  • Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
  • Build machine learning based regression models and test their robustness in R
  • Learn when and how machine learning models should be applied
  • Compare different different machine learning algorithms for regression modelling
Requirements
  • Should have prior experience of working with R and RStudio
  • Should have basic knowledge of statistics
  • Should have prior experience of using simple linear regression modelling
  • Should have interest in building on the previous concepts to learn which regression models are applicable under different circumstances
  • Should have an interest in learning the machine learning based regression models in R
            With so many R Statistics & 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 manner. It explores the relevant concepts  in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.

My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data.  Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. 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. 

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