# mlprac - Machine Learning Practice Notebooks
A comprehensive collection of Jupyter notebooks for practicing machine learning concepts with Python, NumPy, Pandas, and Scikit-learn.
## Installation
Install the package using pip:
```bash
pip install mlprac
```
## Usage
### Download Notebooks
After installation, you can download all practice notebooks to your current working directory:
```bash
mlprac download
```
Or specify a custom destination:
```bash
mlprac download --dest my-notebooks
```
### List Available Notebooks
To see all available notebooks without downloading:
```bash
mlprac download --list
```
### Package Information
Get information about the package:
```bash
mlprac info
```
## Notebook Contents
This package includes 30+ Jupyter notebooks covering:
- **NumPy Basics** (2.ipynb, 2a-2d.ipynb)
- Array creation and manipulation
- Indexing and slicing
- Mathematical operations
- Broadcasting
- **Linear Regression** (3.ipynb, 3a-3b.ipynb, 3multi.ipynb)
- Simple linear regression
- Multiple linear regression
- Model evaluation
- **Classification Algorithms** (4.ipynb - 7.ipynb series)
- Logistic regression
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Decision Trees
- **Clustering** (8.ipynb series)
- K-Means clustering
- Hierarchical clustering
- **Neural Networks** (9.ipynb series)
- Basic neural network implementations
- Deep learning concepts
- **Advanced Topics** (10.ipynb series)
- Ensemble methods
- Model optimization
## Requirements
The notebooks use the following Python libraries:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
Install them separately:
```bash
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
```
## Python API
You can also use the package programmatically in Python:
```python
import mlprac
# Get the path to notebooks
notebooks_path = mlprac.get_notebooks_path()
# List all available notebooks
notebooks = mlprac.list_notebooks()
for nb in notebooks:
print(nb)
```
## License
MIT License
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"description": "# mlprac - Machine Learning Practice Notebooks\r\n\r\nA comprehensive collection of Jupyter notebooks for practicing machine learning concepts with Python, NumPy, Pandas, and Scikit-learn.\r\n\r\n## Installation\r\n\r\nInstall the package using pip:\r\n\r\n```bash\r\npip install mlprac\r\n```\r\n\r\n## Usage\r\n\r\n### Download Notebooks\r\n\r\nAfter installation, you can download all practice notebooks to your current working directory:\r\n\r\n```bash\r\nmlprac download\r\n```\r\n\r\nOr specify a custom destination:\r\n\r\n```bash\r\nmlprac download --dest my-notebooks\r\n```\r\n\r\n### List Available Notebooks\r\n\r\nTo see all available notebooks without downloading:\r\n\r\n```bash\r\nmlprac download --list\r\n```\r\n\r\n### Package Information\r\n\r\nGet information about the package:\r\n\r\n```bash\r\nmlprac info\r\n```\r\n\r\n## Notebook Contents\r\n\r\nThis package includes 30+ Jupyter notebooks covering:\r\n\r\n- **NumPy Basics** (2.ipynb, 2a-2d.ipynb)\r\n - Array creation and manipulation\r\n - Indexing and slicing\r\n - Mathematical operations\r\n - Broadcasting\r\n\r\n- **Linear Regression** (3.ipynb, 3a-3b.ipynb, 3multi.ipynb)\r\n - Simple linear regression\r\n - Multiple linear regression\r\n - Model evaluation\r\n\r\n- **Classification Algorithms** (4.ipynb - 7.ipynb series)\r\n - Logistic regression\r\n - K-Nearest Neighbors (KNN)\r\n - Support Vector Machines (SVM)\r\n - Decision Trees\r\n\r\n- **Clustering** (8.ipynb series)\r\n - K-Means clustering\r\n - Hierarchical clustering\r\n\r\n- **Neural Networks** (9.ipynb series)\r\n - Basic neural network implementations\r\n - Deep learning concepts\r\n\r\n- **Advanced Topics** (10.ipynb series)\r\n - Ensemble methods\r\n - Model optimization\r\n\r\n## Requirements\r\n\r\nThe notebooks use the following Python libraries:\r\n- NumPy\r\n- Pandas\r\n- Matplotlib\r\n- Seaborn\r\n- Scikit-learn\r\n\r\nInstall them separately:\r\n\r\n```bash\r\npip install numpy pandas matplotlib seaborn scikit-learn jupyter\r\n```\r\n\r\n## Python API\r\n\r\nYou can also use the package programmatically in Python:\r\n\r\n```python\r\nimport mlprac\r\n\r\n# Get the path to notebooks\r\nnotebooks_path = mlprac.get_notebooks_path()\r\n\r\n# List all available notebooks\r\nnotebooks = mlprac.list_notebooks()\r\nfor nb in notebooks:\r\n print(nb)\r\n```\r\n\r\n## License\r\n\r\nMIT License\r\n",
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