Expected Learning Outcomes
· Implement and infer Ordinary Least Square (OLS) Regression using R
· Apply statistical and machine learning based regression models to deals with problems such as multi-collinearity
· Carry out variable selection and assess model accuracy using techniques like cross-validation
· Implement and infer Generalized Linear Models (GLMS), using logistic regression as a binary classifier
· Build Machine Learning based regression models and test their robustness in R
· Learn accurate application of Machine Learning models
· Compare different Machine Learning algorithms for regression modelling
Regression analysis is one of the core aspects of both statistical and machine learning based analysis. With this course, you will learn regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explains the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you a new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions.
This course is based on years of regression modelling experience and implementing different regression models on real life data. This course does not merely teach the students about basic aspects of regression analysis, you will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multi-collinearity 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
· Perform 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 multi-collinearity both through variable selection and regularization techniques such as ridge regression
· Perform 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.
· Perform model selection
Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data
This course will help you acquire the knowledge of statistical and machine learning analysis. Specifically, the course will:
a) Teach the students with a basic level of statistical knowledge to perform some of the most common advanced regression analysis based techniques
b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks
c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation
d) Provide students with a strong background in some of the most important statistical and machine learning concepts for regression analysis
e) Teach students to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results
It is a practical, applied course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each section, you will learn a new concept or technique which you may apply to your own projects.