Coursera Linear Regression Courses List

This course will teach you how to use the R programming language to perform linear regression analysis for public health research. This course provides an introduction to linear regression in R, covering topics such as data exploration, model building, and interpretation of results. Participants will gain the skills to apply linear regression to public health research. View all

This course provides an introduction to linear regression models, allowing students to assess the relationship between variables in a data set and a continuous response variable. Through data examples, students will learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio. View all

Regression Analysis is a powerful tool used in Business Statistics to forecast and predict outcomes. It is used in a variety of applications to gain insights from data and make informed decisions. Linear Regression is a key component of this process. View all

This course introduces the classical linear regression model, which is used to analyze the relationship between a dependent variable and one or more independent variables. It covers topics such as hypothesis testing, model specification, and interpretation of results. View all

This course teaches students how to use linear algebra to build regression models. Through hands-on practice with Python, students will learn to distinguish between different types of regression models and apply the Method of Least Squares to datasets. They will also gain the skills to identify scenarios using linear regression models. View all

This course provides learners with an introduction to advanced statistical modeling tools, such as GLMs, nonparametric modeling, and GAMs. It emphasizes a strong conceptual understanding of these tools, as well as ethical considerations when using them. View all

This project-based course on Linear Regression with NumPy and Python is the perfect way to gain a deeper understanding of the fundamentals of machine learning. With Coursera's Rhyme platform, you can do projects in a hands-on manner in your browser with instant access to pre-configured cloud desktops containing all the software and data you need. No need to worry about setting up the environment, just focus on learning and mastering linear regression with Python and NumPy. Join now and start your journey to becoming a machine learning expert! View all

This 2-hour long project-based course is perfect for those who want to learn how to implement Linear Regression using Python and Numpy. It is an important concept for those who want to break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, this course will focus on the practical aspect of implementing linear regression with gradient descent. It is best suited for learners based in the North America region, with plans to expand to other regions. View all

This guided project is perfect for absolute beginners who want to learn the basics of simple linear regression. It is a hands-on project that will teach you the fundamentals of linear regression and how to apply it to real-world problems. You will learn how to use linear regression to predict the value of one variable based on another variable. By the end of the project, you will have a portfolio of projects that will be essential for your next job interview. So don't miss out and join this project now! View all

In diesem zweistündigen Projekt wirst du lernen, wie du ein einfaches lineares Regressionsmodell in R erstellst und wie du es verwendest, um ein grundlegendes Regressionsproblem zu lösen. Ein solches Modell benutzt genau eine Variable, um genau eine andere Zielvariable vorherzusagen. Am Ende dieses Projekts wirst du ein Regressionsmodell erstellt, trainiert, getestet, und visualisiert haben. Dieses Modell wird in der Lage sein, das Gehalt von Data Scientists vorherzusagen, wenn man ihm Informationen über die Erfahrungsjahre gibt. View all