Linear and Logistic Regression

Course

The linear and logistic regression course offers a detailed introduction to fundamental statistical and machine learning algorithms, particularly focusing on regression techniques. The course begins with simple linear regression and progresses to multiple linear regression, equipping students with the ability to analyze relationships between multiple variables. Finally, it covers logistic regression, a powerful tool for classification problems.

Intermediate
4 weeks
Last Updated January 14, 2025

Skills you'll learn:

Statistical modeling fundamentalsLogistic regression

Prerequisites:

Basic probabilityPandasInferential statisticsBasic PythonNumPy

Intermediate

4 weeks

Last Updated January 14, 2025

Skills you'll learn:

Statistical modeling fundamentals • Logistic regression • Linear regression • Linear functions

Prerequisites:

Basic probability • Pandas • Inferential statistics

Course Lessons

Lesson 1

Introduction

In this lesson, we kick off the course with an introduction to machine learning and why linear regression is such an important algorithm.

Lesson 2

Regression

In this lesson, you'll use Python to fit linear regression models, as well as understand how to interpret the results of linear models.

Lesson 3

Multiple Linear Regression

In this lesson, you'll learn to apply multiple linear regression models in Python. Then you'll learn how to interpret the results and understand if your model fits well.

Lesson 4

Logistic Regression

In this lesson, you'll learn to apply logistic regression models in Python as well as how to interpret logistic regression results.

Lesson 5 • Project

Model Salary Data with Linear Regression

In this project, you will build a linear model and interpret the results of the model. Specifically, you will look at what variables are related to salaries.

Taught By The Best

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.