Create a linear model, and implement gradient descent.
Train the linear model to fit given data using gradient descent.
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process. Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
This was my first guided project on coursera and I decided to go with something small. This project just motivated me to take up more projects on the platform
Good refresher course on linear regression! It would have been great had the Instructor covered few of the statical tests or multivariate regression model.
overall course was good but i certificate i mention course have completed , it would be good if project like statement have mentioned
Great course. Learn new topics like forward passing and backward passing to update parameters for prediction in regression
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