In this tutorial, we are covering few important concepts in machine learning such as cost function, gradient descent, learning rate and mean squared error. We will use home price prediction use case to understand gradient descent. After going over math behind these concepts, we will write python code to implement gradient descent for linear regression in python. At the end I've an an exercise for you to practice gradient descent

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Code: https://github.com/codebasics/py/blob...
Exercise csv file: https://github.com/codebasics/py/blob...

Topics that are covered in this Video:
0:00 Overview
1:23 - What is prediction function? How can we calculate it?
4:00 - Mean squared error (ending time)
4:57 - Gradient descent algorithm and how it works?
11:00 - What is derivative?
12:30 - What is partial derivative?
16:07 - Use of python code to implement gradient descent
27:05 - Exercise is to come up with a linear function for given test results using gradient descent

Topic Highlights:
1) Theory (We will talk about MSE, cost function, global minima)
2) Coding - (Plain python code that finds out a linear equation for given sample data points using gradient descent)
3) Exercise - (Exercise is to come up with a linear function for given test results using gradient descent)

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