https://github.com/rvats20/ml-scikit-learn

This repo contains notebooks to learn ML Models and apply them in real world problems.
https://github.com/rvats20/ml-scikit-learn

artificial-intelligence classification deep-learning deep-neural-networks machine-learning machine-learning-algorithms notebook notebook-jupyter

Last synced: about 14 hours ago
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This repo contains notebooks to learn ML Models and apply them in real world problems.

• Host: GitHub
• URL: https://github.com/rvats20/ml-scikit-learn
• Owner: rvats20
• Created: 2024-11-05T14:56:56.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-06T13:43:49.000Z (3 months ago)
• Last Synced: 2024-12-13T13:16:52.394Z (about 2 months ago)
• Topics: artificial-intelligence, classification, deep-learning, deep-neural-networks, machine-learning, machine-learning-algorithms, notebook, notebook-jupyter
• Language: Jupyter Notebook
• Homepage: https://rvats20.github.io/portfolio
• Size: 127 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# ML-Scikit Learning Resource

Welcome to the ML-Scikit Learning Resource repository! This repository contains a collection of tutorials, examples, and projects to help you get started with machine learning using Scikit-learn.

## Table of Contents

- Introduction

- Installation

- Linear Regression

- Logistic Regression

- Decision Tree

- Random Forest

- K means Clustring

- Bagged Tree

- Train Test Split

- Contributing

- License

## Introduction

Scikit-learn is a powerful and easy-to-use library for machine learning in Python. This repository aims to provide a comprehensive set of resources to help you learn and apply machine learning techniques using Scikit-learn.

## Installation

To get started, you'll need to install Scikit-learn. You can do this using pip:

```bash

pip install scikit-learn

```

## Usage

This repository includes various examples and tutorials. Here are some of the key resources:

- **Tutorials**: Step-by-step guides to help you understand the basics of machine learning with Scikit-learn.

- **Examples**: Practical examples demonstrating how to use Scikit-learn for different machine learning tasks.

- **Projects**: Real-world projects to apply your knowledge and build machine learning models.

## Contributing

We welcome contributions! If you have any tutorials, examples, or projects you'd like to share, please follow these steps:

1. Fork the repository.

2. Create a new branch (`git checkout -b feature-branch`).

3. Commit your changes (`git commit -m 'Add new resource'`).

4. Push to the branch (`git push origin feature-branch`).

5. Open a pull request.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.

Happy learning! If you have any questions or suggestions, feel free to open an issue or contact us.