Linear Regression Courses and Certifications

UofU Data Science

Linear Regression: Least Mean Squares Method and Gradient Descent - Lecture 10

Master linear regression through least mean squares method, exploring loss function minimization and gradient descent techniques for effective machine learning implementation.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Codeynamics

Multiple Linear Regression with Scikit-learn in Python - Supervised Machine Learning

Master multiple linear regression techniques in Python using scikit-learn, from data preprocessing to model evaluation and prediction implementation.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

AnalystPrep

Linear Regression - FRM Part 1 2025 Book 2 Chapter 7

Master linear regression fundamentals for FRM Part 1: OLS estimation, hypothesis testing, confidence intervals, and key assumptions with practical applications.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

UofU Data Science

Foundations of Data Analysis - Linear Regression - Lecture 11

Master linear regression fundamentals: prediction functions, sum of squared errors, RMSE, and algorithms for 1-dimensional data analysis.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Introduction to Predictive Modeling

Builds a first predictive model on the California Housing dataset: scatter plots, linear regression fitted with scikit-learn, and MSE-based evaluation.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

PredictHealth's Multi-Factor Cost Models

Develop multi-factor insurance cost models by combining categorical variables, robust preprocessing, cross-validation, and custom feature engineering.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Linear Regression Concept and with R Video Series

Learn linear regression concepts and apply them in R, including assumptions, categorical predictors, interactions, polynomial models, and variable selection.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

Linear Regression in R

Learn to fit, assess, and interpret linear regression models in R, including categorical variables, interactions, model selection, and polynomial terms.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

jbstatistics

Simple Linear Regression

Learn to fit, interpret, assess, and make inferences from simple linear regression models using least squares, residual plots, transformations, and influential-point analysis.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

statisticsmatt

General Linear Models - Regression

A theory-focused treatment of simple and multiple linear regression, from least-squares inference and matrix methods to multicollinearity, ridge regression, diagnostics, and model selection.

• YouTube
• 17 hours
• Self-Paced
• Free Video

Professor Knudson

Linear Regression Essentials

Develops simple and multiple linear regression using matrix notation, covering estimator properties, coefficient inference, mean squared error, R-squared, and adjusted R-squared.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Supervised Machine Learning

This practical course is designed for individuals who utilise data to make informed decisions and wish to enhance their decision-making skills.

• OpenLearning
• 20 hours
• Self-Paced
• Paid Course

Mastering Data Analysis in Excel

27 ratings

Build predictive models in Excel: score credit card applicants with binary classification and AUC, quantify uncertainty with entropy, and forecast with linear regression and confidence intervals.

• Coursera
• 7 weeks, 3 hours a week
• Self-Paced
• Paid Course

Advanced Linear Models for Data Science 1: Least Squares

192 ratings at Coursera

Study least squares from a linear algebraic perspective: matrix summary statistics, regression through the origin, general least squares with full rank design matrices, and basis expansions.

• Coursera
• 6 weeks, 1 hour a week
• Self-Paced
• Paid Course

Advanced Linear Models for Data Science 2: Statistical Linear Models

108 ratings at Coursera

Study least squares from a linear algebraic angle: expected values of multivariate vectors, the multivariate and singular normal distribution, distributional results, and PRESS residuals.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course