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Building Regression Models with Linear Algebra

Howard University via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 3 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English, Arabic, German, Korean, Japanese, Italian, Indonesian, French, Spanish

Found in

Part of

Linear Algebra for Data Science Using Python

1.0

Overview

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In this course, you'll learn how to distinguish between the different types of regression models. You will apply the Method of Least Squares to a dataset by hand and using Python. In addition, you will learn how to employ a linear regression model to identify scenarios. Let's get started!

Syllabus

Introduction to Regression Models
- In module 1, you’ll learn how to define regression and learn about the various types of regression models and how they are used. We will cover the following learning objectives.
Using the Method of Least Squares
- Let’s recap! In module 1, you learned how to define regression models and use the various types of regression models. In module 2, you’ll gain the knowledge you need to know in order to apply the method of least squares. You’ll also learn how to apply the method of least squares using Python. We will cover the following learning objectives.
Using Linear Regression Models
- Let’s recap! In module 2, you learned how to apply the method of least squares. In module 3, you will learn how to understand linear regression models. We will cover the following learning objectives.
Using Linear Regression Model
- Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: regression, regression models, applying the method of least squares and, understanding linear regression models. In the final module of the course, you’ll apply what you’ve learned to concrete, real-world examples. You’ll review real-world linear regression models and complete peer reviews. We will cover the following learning objectives.