https://github.com/mstryoda/ml-from-scratch

A comprehensive collection of practical machine learning examples using popular frameworks and libraries.
https://github.com/mstryoda/ml-from-scratch

llm machine-learning matplotlib numpy pandas python pytorch sklearn

Last synced: 1 day ago
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A comprehensive collection of practical machine learning examples using popular frameworks and libraries.

• Host: GitHub
• URL: https://github.com/mstryoda/ml-from-scratch
• Owner: mstrYoda
• Created: 2025-02-05T11:25:00.000Z (10 days ago)
• Default Branch: main
• Last Pushed: 2025-02-09T17:41:54.000Z (5 days ago)
• Last Synced: 2025-02-13T05:30:00.087Z (2 days ago)
• Topics: llm, machine-learning, matplotlib, numpy, pandas, python, pytorch, sklearn
• Language: Python
• Homepage:
• Size: 60.5 KB
• Stars: 117
• Watchers: 5
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Examples Collection

A comprehensive collection of practical machine learning examples using popular frameworks and libraries. This repository serves as a learning resource and reference for both beginners and experienced practitioners.

## Repository Structure

### 1. NumPy Examples (`numpy_examples/`)

Fundamental numerical computing examples:

- Array operations and manipulation

- Broadcasting and vectorization

- Linear algebra operations

- Random number generation

- Mathematical functions

- Performance optimization

- Memory management

### 2. Matplotlib Examples (`matplotlib_examples/`)

Data visualization examples:

- Basic plotting techniques

- Advanced plot customization

- Statistical visualizations

- Interactive plots

- 3D plotting

- Animation

- Custom styling

- Multiple subplots

### 3. Pandas Examples (`pandas_examples/`)

Examples for data manipulation and analysis:

- Data cleaning and preprocessing

- Data analysis and grouping

- Time series analysis

- Data visualization

- Advanced operations

- Merging and joining

- Performance optimization

### 4. Scikit-learn Examples (`sklearn_examples/`)

Collection of examples demonstrating classical machine learning techniques:

- Basic classification and regression

- Feature engineering and selection 

- Model evaluation and tuning

- Ensemble methods

- Clustering and dimensionality reduction

- Time series analysis

- Handling imbalanced data

- Model deployment

### 5. HuggingFace Examples (`huggingface_examples/`)

Examples for working with transformer models and NLP tasks:

- Model fine-tuning

- Custom training loops

- Advanced training techniques

- Model evaluation and inference

- Deployment strategies

### 6. PyTorch Examples (`pytorch_examples/`)

Examples showcasing deep learning with PyTorch:

- Basic tensor operations

- Neural network implementations

- CNN architectures

- Transfer learning

- Custom datasets and dataloaders

- GPU acceleration

- Model optimization

## Getting Started

### Prerequisites

- Python 3.8+

- pip or conda for package management

## Acknowledgments

- Open source ML community

- Framework and library developers

- Dataset providers

- Contributors and users

## Contact

For questions and feedback:

- Create an issue in the repository

- Contact maintainers directly

- Join our community discussions

## Future Plans

- Add more interactive visualizations

- Include deep learning visualization examples

- Add reinforcement learning examples

- Expand deployment examples

- Include MLOps examples

- Add AutoML examples

- Include more real-world case studies

- Add GPU optimization examples