Coursera MOOC / Non-credit 0

Regression Analysis for Statistics & Machine Learning in R

Name: Regression Analysis for Statistics & Machine Learning in R
Price: 49 USD
Availability: InStock

About this course

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course delves into regression analysis using R, covering key concepts, software tools, and differences between statistical analysis and machine learning. - You'll learn data reading, cleaning, exploratory data analysis, and ordinary least squares (OLS) regression modeling, including theory, implementation, and result interpretation. - You'll tackle multicollinearity with techniques like principal component regression and LASSO regression, and cover variable and model selection for performance evaluation. - You'll handle OLS violations through data transformations and robust regression, and explore generalized linear models (GLMs) for logistic regression and count data analysis. - Advanced sections include non-linear and non-parametric techniques such as polynomial regression, GAMs, regression trees, and random forests. Ideal for statisticians, data analysts, and machine learning practitioners with basic R knowledge, this course blends theory with hands-on practice to enhance your regression analysis skills.

68/100

CourseAsk score

What the provider tells you: 32/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

reading and cleaning data
performing exploratory data analysis
implementing ordinary least squares (OLS) regression modeling
tackling multicollinearity with techniques such as principal component regression and LASSO regression
handling OLS violations through data transformations and robust regression
exploring generalized linear models (GLMs) for logistic and count data analysis
using non-linear and non-parametric techniques like polynomial regression and regression trees

Machine Learning Data Analysis #machine learning #data visualization #regression analysis #exploratory data analysis #data cleaning #random forests #ordinary least squares #multicollinearity #principal component regression #LASSO regression #generalized linear models #non-linear regression #regression trees

$49.00

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