https://github.com/praju-1/machine_learning

Exploring Machine learning with its supervised and unsupervised algorithm and subtypes also.. All algorithm implemented in python With proper description of each Dataset used.
https://github.com/praju-1/machine_learning

machine-learning machine-learning-algorithms matplotlib numpy pandas-library python sklearn-library statistics

Last synced: 25 days ago
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Exploring Machine learning with its supervised and unsupervised algorithm and subtypes also.. All algorithm implemented in python With proper description of each Dataset used.

• Host: GitHub
• URL: https://github.com/praju-1/machine_learning
• Owner: praju-1
• Created: 2023-01-02T14:29:24.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-17T10:16:26.000Z (11 months ago)
• Last Synced: 2024-02-17T11:28:57.017Z (11 months ago)
• Topics: machine-learning, machine-learning-algorithms, matplotlib, numpy, pandas-library, python, sklearn-library, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 9.07 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine_Learning

Welcome to the Machine Learning Repository! 

This repository is dedicated to housing various machine learning projects, algorithms, and resources, aimed at providing a comprehensive collection of machine learning implementations and educational materials.

REPOSITORY STRUCTURE

* This repository is structured to provide a user-friendly experience and easy access to different machine learning resources. Here's an overview of the main components:

1. Algorithms: This directory contains implementations of popular machine learning algorithms. Each algorithm is organized in its own directory and includes code, documentation, and examples to facilitate understanding and usage.

2. Projects: The projects directory showcases end-to-end machine learning projects, including datasets, notebooks, and associated resources. These projects demonstrate how to apply machine learning techniques to real-world problems and provide insights into best practices for project organization and implementation.

Feel free to experiment, modify, and extend the code to suit your own projects and research. Contributions are welcome, as this repository aims to foster collaboration and knowledge sharing among the machine learning community.