Linear Regression with Python by Coursera

Course Cover
compare button icon

Course Features

icon

Duration

2 hours

icon

Delivery Method

Online

icon

Available on

Limited Access

icon

Accessibility

Desktop, Laptop

icon

Language

English

icon

Subtitles

English

icon

Level

Intermediate

icon

Teaching Type

Self Paced

icon

Video Content

2 hours

Course Description

This 2-hour-long project-based course will teach you how to implement Linear Regression with Python and Numpy. If you are looking to get into Machine Learning or Deep Learning, Linear Regression will be a key concept. Although many machine learning frameworks offer linear regression implementations, it is still beneficial to learn how to implement it yourself to get a better understanding of the optimization algorithm and the training process. This course is practical and project-based. You will need to be familiar with linear regression and gradient descent. We will be focusing on the practical aspects of implementing linear regression using gradient descent.

Course Overview

projects-img

Virtual Labs

projects-img

International Faculty

projects-img

Case Based Learning

projects-img

Post Course Interactions

projects-img

Case Studies,Instructor-Moderated Discussions

projects-img

Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Train the linear model to fit given data using gradient descent

Create a linear model, and implement gradient descent

Course Cover