Regression Analysis for Statistics & Machine Learning in R (Udemy.com)
Created by: Minerva Singh
Produced in 2021
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Overall Score : 92 / 100
Course Description
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.
Become a Regression Analysis Expert and Harness the Power of R for Your Analysis
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.
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 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 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 models
- Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.
- Carry out model selection
- People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
- People with basic knowledge of R based statistical modelling
- People with knowledge of linear regression modelling
- People wanting to extend their knowledge of regression modelling for solving real world problems.
- People wanting to learn how to apply machine learning based regression models using R
- Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
- Academic researchers seeking to learn new techniques for data analysis
- Business data analysts who wish to use regression modelling for predictive analysis
Instructor Details
- 4.6 Rating
295 Reviews
Minerva Singh
Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I domost of my spatial data analysis work using R and QGIS.Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being ascientist and number cruncher, I am an avid traveler
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- Provider: Udemy
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