Building a Linear Regression Model from Scratch with Gradient Descent in Python
This article explains how to build a linear regression model from scratch using gradient descent in Python. It covers the essential concepts of parameters, loss, gradients, and optimization.
Why it matters
This tutorial provides a hands-on introduction to building a simple machine learning model from scratch, which is a valuable skill for any aspiring data scientist or machine learning engineer.
Key Points
- 1Implements a linear regression model to minimize mean-squared error between predictions and true outputs
- 2Starts with initial slope and intercept values of 0 and updates them using gradient descent
- 3Explains the math behind the gradient descent algorithm and how to tweak the hyperparameters
Details
The article walks through a Python script that trains a linear regression model on a small dataset of 5 data points. It starts with initial slope and intercept values of 0 and uses gradient descent to iteratively update these parameters to minimize the mean-squared error between the model's predictions and the true outputs. The article explains the mathematical concepts behind gradient descent and how to adjust the learning rate and number of iterations. By the end, the script prints the learned slope, intercept, and makes a prediction for a new input.
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