Linear regression is a fundamental machine learning algorithm used for predictive modeling and data analysis. Learn more about it as Codecademy Curriculum Developers guide you through our course: Linear Regression in Python

Linear Regression with Python

Welcome to linear regression with Codecademy!
In our final session, Curriculum Developers Sophie and Alex finish walking us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/3ofd5pY

Have questions about this livestream? Join us for office hours on our Discord server Thursday, July 15 at 11:00 AM: https://bit.ly/3px5QKy

More details:
In our final session, we'll walk through the full process of pre-processing some data, fitting a few different linear models, choosing the "best" one, interpreting the results, and using our model to make predictions for new data. We hope this session inspires you to build your own linear regression model to solve a problem that interests you!

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Codecademy Live Linear Regression #8: Linear Regression Workflow

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Alex will continue to walk us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/2QecF6y

Have questions about this livestream? Join us for office hours on our Discord server Thursday, July 1 at 11:00 AM: https://bit.ly/3px5QKy

More details:
In our seventh session, we'll discuss ways of comparing possible regression models and choosing the "best" one for prediction or analysis. In different business and research settings, the word "best" might mean different things. For example, if you need to be able to explain your model to a broad audience, you may want to prioritize interpretability over small gains in accuracy. By the end of this session, you'll understand some of the most common methods for choosing a model.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Codecademy Live Linear Regression #7: Comparing and Choosing a Linear Regression Model

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Alex will continue to walk us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/33HACXe

Have questions about this livestream? Join us for office hours on Thursday, June 24 at 11:00 AM on our Discord server: https://bit.ly/3px5QKy

More details:
In our sixth session, we'll learn about data pre-processing that is useful prior to fitting a linear regression model. These kinds of transformations are potentially helpful when the assumptions of linear regression are otherwise violated. Pre-processing can also make the regression output more interpretable and therefore easier to communicate to a non-technical audience.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Codecademy Live Linear Regression #6: Data Transformations

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Alex will continue to walk us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/3uMYHrk

Have questions about this livestream? Join us for office hours on our Discord server Thursday, June 17 at 11:00 AM EDT: https://bit.ly/3px5QKy

More details:
In our fifth session, we'll learn about some useful tools for modeling non-linear relationships using linear regression. This will allow us to build more flexible models, representing real-life relationships that cannot be summarized with a straight line.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Learn to code: https://j.mp/3w4Ra7z
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Codecademy Live Linear Regression #5: Interactions and Polynomial Terms

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Nitya will continue to walk us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/3tQO4mk

More details:
In our fourth session, we'll dig into some of the math behind multiple linear regression. While there are a number of Python modules that allow us to fit a model without understanding this math, it is difficult to troubleshoot, interpret, and learn new technologies without it. This session will give you enough understanding to accomplish these things, while still bypassing detailed algebra and calculus!

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Check out our full course catalog: https://j.mp/33E6NXh

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Join us for office hours on Thursdays on discord:
https://discord.gg/codecademy

Codecademy Live Linear Regression #4: The Matrix Representation of the Linear Regression Problem

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Jamie will continue to walk us through our Linear Regression in Python Course. Check out the full course here: https://j.mp/3uGSdu6

More details:
In our third session, we'll build our first linear model with more than one predictor. As an example, we'll use simulated data from a math class to predict student quiz performance based on hours of studying, number of completed assignments, and whether or not the student ate breakfast.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Codecademy Live Linear Regression #3: Introduction To Multiple Linear Regression

Welcome to linear regression with Codecademy!
Curriculum Developers Sophie and Alex will continue to walk us through our Linear Regression in Python course. Check out the full course here: https://j.mp/3bprVVE

More details:
In our second session, we'll demonstrate how to include a categorical predictor with two or more categories in a simple linear model. For example, we'll learn how to predict the price of a New York City apartment based on the borough where it is located. This will help us build our intuition for how to create and interpret more complex linear models.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series

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Learn to code: https://j.mp/3uKHJK9
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Codecademy Live Linear Regression #2: Categorical Predictors

Welcome to linear regression with Codecademy!
In our first session, Curriculum Developers Sophie and Alex will start to walk us through Codecademy's Linear Regression in Python course. Check out the full course here: https://j.mp/3eLjLZM

More details
In our first session, Sophie and Alex will introduce the concept of simple linear regression and learn how to implement it in Python. Linear regression is a machine learning technique that is used to predict and analyze quantitative outcomes, such as salary, time spent on a website, or adult height. In the process, we'll review algebra and graphing skills and learn about concepts that are applicable to many different machine learning algorithms.

The code used in this video can be found here: https://github.com/Codecademy/Linear-Regression-Live-Series 

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Join the millions learning to code with Codecademy.
Learn to code: https://j.mp/3y86Eto
Check out our full course catalog: https://j.mp/33G5reP

Codecademy Live Linear Regression #1: Introduction To Simple Linear Regression

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