Welcome to the AI Projects Repository! This repository is designed for AI and data science enthusiasts to contribute and collaborate on various artificial intelligence projects. We aim to create a diverse range of AI projects, from machine learning models to advanced deep learning applications, while fostering an open-source community.
This repository hosts a variety of AI and data science projects, including:
- Machine Learning Models
- Deep Learning Applications
- Data Analysis Scripts
- AI Research Implementations
We encourage collaboration from contributors of all skill levels and offer clear guidelines for getting involved.
Before contributing or using any of the projects, ensure you have the following installed:
- Python 3.8+
- Git
- Jupyter Notebook (optional for running notebooks)
- Virtual Environment (recommended for package management)
This repository includes the following projects:
Implements a supervised learning model to predict XYZ using scikit-learn. Key techniques: data preprocessing, feature engineering, model evaluation.
Uses CNNs to classify images from the ABC dataset using TensorFlow/Keras. Techniques: convolutional layers, pooling, dropout, softmax classifier.
Natural Language Processing model to analyze the sentiment of product reviews. Libraries: spaCy, NLTK.
Our repository includes interactive visualizations that help to better understand model performance:
Model accuracy and loss over epochs Confusion matrices Precision-Recall and ROC curves Below is an example plot showing training performance:
We welcome contributions from the community! To contribute, follow these steps:
Create a new branch for your contribution. Commit your changes with a meaningful commit message. Open a pull request and describe the changes you’ve made.
For detailed contribution instructions, please read the CONTRIBUTING.md.