# ml_tutorial

**Repository Path**: junjun_com/ml_tutorial

## Basic Information

- **Project Name**: ml_tutorial
- **Description**: 机器学习教程、机器学习从入门到精通（人工智能大模型必学）
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 1
- **Created**: 2025-12-19
- **Last Updated**: 2026-06-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: Python, AI

## README

This project appears to be missing a formal README.md file. Below is a preliminary README.md template tailored for a machine learning tutorial project, based on its structure and content.

---

# Machine Learning Tutorial (ml_tutorial)

This project is a hands-on machine learning learning repository, containing multiple chapters that cover a comprehensive range of topics from foundational theory to practical applications. It is suitable for beginners getting started with machine learning, as well as developers with some background seeking practical exercises.

## Project Structure

- **ch02_base**: Fundamental concepts and tool usage, including linear regression, gradient descent, feature selection, and evaluation metrics.
- **ch03_knn**: Applications of the K-Nearest Neighbors algorithm for classification and regression.
- **ch04_linear_regression**: Linear regression models and gradient descent optimization methods.
- **ch05_logistic_regression**: Logistic regression and its application in classification tasks.
- **ch06_perceptron**: Perceptron algorithm and implementation of logic gates.
- **ch07_other_supervised**: Additional supervised learning concepts.
- **ch08_unsupervised**: Unsupervised learning methods such as KMeans and SVD.
- **data**: Dataset files used in the project.
- **notebook**: Supplementary Jupyter Notebook files for learning assistance.

## Environment Setup

- Python 3.x
- Jupyter Notebook (required to run `.ipynb` files)
- Common machine learning libraries (e.g., scikit-learn, numpy, pandas, matplotlib)

Install dependencies:

```bash
pip install numpy pandas matplotlib scikit-learn jupyter
```

## How to Use

1. Clone this project to your local machine:

   ```bash
   git clone https://gitee.com/junjun_com/ml_tutorial.git
   ```

2. Navigate to the project directory and launch Jupyter Notebook (if needed):

   ```bash
   cd ml_tutorial
   jupyter notebook
   ```

3. Follow the chapter order to progressively run and study the content of each `.ipynb` or `.py` file.

## Notes

- All `.csv` data files are located in the `data` directory—ensure correct file paths are used.
- This project is designed for learning and practice; it is recommended to complement it with theoretical knowledge of machine learning.

## Contribution

Pull requests and issues are welcome to improve this tutorial. Please follow the project's style and ensure your code is clear and well-documented.

## License

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

---

Please further refine and expand this document based on the actual project details, especially by adding specific information about datasets, models, and learning objectives.