https://github.com/augustine-aj/ml-algorithms-from-scratch
https://github.com/augustine-aj/ml-algorithms-from-scratch
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/augustine-aj/ml-algorithms-from-scratch
- Owner: augustine-aj
- Created: 2024-06-16T08:33:18.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-06T03:47:47.000Z (almost 2 years ago)
- Last Synced: 2025-04-23T17:13:16.580Z (about 1 year ago)
- Language: Python
- Size: 10.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Creating-my-own-ml-algorithms
# Simple Univariate Linear Regression algorithm
You can access the PDF document from the following Google Drive link:
https://drive.google.com/file/d/1TrkHoBFQVj8HJUjYYV2USLsolJADMq9P/view?usp=sharing
## Overview
This repository contains details about the Univariate Linear Regression Algorithm. The algorithm is a fundamental machine learning technique used to model the relationship between a single independent variable and a dependent variable by fitting a linear equation to observed data.
## Content
The detailed explanation of the Univariate Linear Regression Algorithm is provided in a PDF document. The PDF includes,
- Introduction to Linear Regression
- Hypothesis Representation
- Cost Function
- Gradient Descent Algorithm
- Linear Algebra in Regression
- Example and Visualization
- Mathematical Derivations
- Code Implementation
## Google Drive Link
You can access the PDF document from the following Google Drive link:
https://drive.google.com/file/d/1TrkHoBFQVj8HJUjYYV2USLsolJADMq9P/view?usp=sharing
## How to Use
- Download the PDF: Click on the Google Drive link above to view and download the PDF document.
- Study the Algorithm: Go through the document to understand the theoretical concepts and practical implementations of the Univariate Linear Regression Algorithm.
- Implement the Code: Use the provided code examples to implement the algorithm in your own projects.
## Contributing
Contributions are welcome! If you have any improvements or suggestions, feel free to create an issue or submit a pull request.
live coding video and pdf will upload soon....