Fixe readme file

README.md

@@ -1,211 +1,14 @@
-# AI-Algorithms-Made-Easy
-
-**Under Development**
-
-![Under Development](under_development.png?raw=true "Under Development")
-
-Welcome to **AI-Algorithms-Made-Easy**! This project is a comprehensive collection of artificial intelligence algorithms implemented from scratch using **PyTorch**. Our goal is to demystify AI by providing clear, easy-to-understand code and detailed explanations for each algorithm.
-
-Whether you're a beginner in machine learning or an experienced practitioner, this project offers resources to enhance your understanding and skills in AI.
-
----
-
-## Project Description
-
-**AI-Algorithms-Made-Easy** aims to make AI accessible to everyone by:
-
-- **Intuitive Implementations**: Breaking down complex algorithms into understandable components with step-by-step code.
-- **Educational Notebooks**: Providing Jupyter notebooks that combine theory with practical examples.
-- **Interactive Demos**: Offering user-friendly interfaces built with **Gradio** to experiment with algorithms in real-time.
-- **Comprehensive Documentation**: Supplying in-depth guides and resources to support your AI learning journey.
-
-Our mission is to simplify the learning process and provide hands-on tools to explore and understand AI concepts effectively.
-
 ---
-
-## Table of Contents
-
-- [Algorithms Implemented](#algorithms-implemented)
-- [Project Structure](#project-structure)
-- [Installation](#installation)
-- [Usage](#usage)
-- [Contributing](#contributing)
-- [License](#license)
-- [Contact](#contact)
-
----
-
-## Algorithms Implemented
-
-*This project is currently under development. Stay tuned for updates!*
-
-### Supervised Learning (Scikit-Learn)
-#### Regression ([Documentation](docs/Regression_Documentation.md), [Interface](https://huggingface.co/spaces/mboukabous/train_regression), [Notebook](notebooks/Train_Supervised_Regression_Models.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mboukabous/AI-Algorithms-Made-Easy/blob/main/notebooks/Train_Supervised_Regression_Models.ipynb))
-- [Linear Regression](models/supervised/regression/linear_regression.py)
-- [Ridge Regression](models/supervised/regression/ridge_regression.py)
-- [Lasso Regression](models/supervised/regression/lasso_regression.py)
-- [ElasticNet Regression](models/supervised/regression/elasticnet_regression.py)
-- [Decision Tree](models/supervised/regression/decision_tree_regressor.py)
-- [Random Forest (Bagging)](models/supervised/regression/random_forest_regressor.py)
-- [Gradient Boosting (Boosting)](models/supervised/regression/gradient_boosting_regressor.py)
-- [AdaBoost (Boosting)](models/supervised/regression/adaboost_regressor.py)
-- [XGBoost (Boosting)](models/supervised/regression/xgboost_regressor.py)
-- [LightGBM](models/supervised/regression/lightgbm_regressor.py)
-- [CatBoost](models/supervised/regression/catboost_regressor.py)
-- [Support Vector Regressor (SVR)](models/supervised/regression/support_vector_regressor.py)
-- [K-Nearest Neighbors (KNN) Regressor](models/supervised/regression/knn_regressor.py)
-- [Extra Trees Regressor](models/supervised/regression/extra_trees_regressor.py)
-- [Multilayer Perceptron (MLP) Regressor](models/supervised/regression/mlp_regressor.py)
-
-#### Classification ([Documentation](docs/Classification_Documentation.md))
-- [Logistic Regression](models/supervised/classification/logistic_regression.py)
-- [Decision Tree Classifier](models/supervised/classification/decision_tree_classifier.py)
-- [Random Forest Classifier (Bagging)](models/supervised/classification/random_forest_classifier.py)
-- [Extra Trees Classifier](models/supervised/classification/extra_trees_classifier.py)
-- [Gradient Boosting Classifier (Boosting)](models/supervised/classification/gradient_boosting_classifier.py)
-- [AdaBoost Classifier (Boosting)](models/supervised/classification/adaboost_classifier.py)
-- [XGBoost Classifier (Boosting)](models/supervised/classification/xgboost_classifier.py)
-- [LightGBM Classifier (Boosting)](models/supervised/classification/lightgbm_classifier.py)
-- [CatBoost Classifier (Boosting)](models/supervised/classification/catboost_classifier.py)
-- [Support Vector Classifier (SVC)](models/supervised/classification/svc.py)
-- [K-Nearest Neighbors (KNN) Classifier](models/supervised/classification/knn_classifier.py)
-- [Multilayer Perceptron (MLP) Classifier](models/supervised/classification/mlp_classifier.py)
-- [GaussianNB (Naive Bayes Classifier)](models/supervised/classification/gaussian_nb.py)
-- [Linear Discriminant Analysis (LDA)](models/supervised/classification/linear_discriminant_analysis.py)
-- [Quadratic Discriminant Analysis (QDA)](models/supervised/classification/quadratic_discriminant_analysis.py)
-  
-### Unsupervised Learning
-
-- K-Means Clustering
-- Principal Component Analysis (PCA)
-- Hierarchical Clustering
-- Autoencoders
-- Isolation Forest
-- Gaussian Mixture Models
-
-### Deep Learning (DL)
-
-- Convolutional Neural Networks (CNN)
-- Recurrent Neural Networks (RNN)
-- Long Short-Term Memory Networks (LSTM)
-- Gated Recurrent Unit (GRU)
-- Generative Adversarial Networks (GAN)
-- Transformers
-- Attention Mechanisms
-
-### Computer Vision
-
-- Image Classification/Transfer learning (TL)
-- Object Detection
-- Semantic Segmentation
-- Style Transfer
-- Image Captioning
-- Generative Models
-
-### Natural Language Processing (NLP)
-
-- Sentiment Analysis (SA)
-- Machine Translation
-- Named Entity Recognition (NER)
-- Text Classification
-- Text Summarization
-- Question Answering
-- Language Modeling
-- Transformer Models
-
-### Time Series Analysis
-  
-- Time Series Forecasting with RNNs
-- Temporal Convolutional Networks (TCNs)
-- Transformers for Time Series
-
-### Reinforcement Learning
-
-- Q-Learning
-- Deep Q-Networks (DQN)
-- Policy Gradients
-- Actor-Critic Methods
-- Proximal Policy Optimization
-
-### and more ...
-
----
-
-## Project Structure
-
-- **models/**: Contains all the AI algorithm implementations, organized by category.
-- **data/**: Includes datasets and data preprocessing utilities.
-- **utils/**: Utility scripts and helper functions.
-- **scripts/**: Executable scripts for training, testing, and other tasks.
-- **interfaces/**: Interactive applications using Gradio and web interfaces.
-- **notebooks/**: Jupyter notebooks for tutorials and demonstrations.
-- **deploy/**: Scripts and instructions for deploying models.
-- **website/**: Files related to the project website.
-- **docs/**: Project documentation.
-- **examples/**: Example scripts demonstrating how to use the models.
-
----
-
-## Installation
-
-*Installation instructions will be provided once the initial release is available.*
-
----
-
-## Usage
-
-*Usage examples and tutorials will be added as the project develops.*
-
----
-
-## Contributing
-
-We welcome contributions from the community! To contribute:
-
-1. **Fork the repository** on GitHub.
-2. **Clone your fork** to your local machine.
-3. **Create a new branch** for your feature or bug fix.
-4. **Make your changes** and commit them with descriptive messages.
-5. **Push your changes** to your forked repository.
-6. **Open a pull request** to the main repository.
-
-Please read our [Contributing Guidelines](CONTRIBUTING.md) for more details.
-
----
-
-## License
-
-This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
-
----
-
-## Contact
-
-For questions, suggestions, or feedback:
-
-- **GitHub Issues**: Please open an issue on the [GitHub repository](https://github.com/mboukabous/AI-Algorithms-Made-Easy/issues).
-- **Email**: You can reach us at [m.boukabous95@gmail.com](mailto:m.boukabous95@gmail.com).
-
----
-
-*Thank you for your interest in **AI-Algorithms-Made-Easy**! We are excited to build this resource and appreciate your support and contributions.*
-
----
-
-## Acknowledgments
-
-- **PyTorch**: For providing an excellent deep learning framework.
-- **Gradio**: For simplifying the creation of interactive demos.
-- **OpenAI's ChatGPT**: For assistance in planning and drafting project materials.
-
----
-
-## Stay Updated
-
-- **Watch** this repository for updates.
-- **Star** the project if you find it helpful.
-- **Share** with others who might be interested in learning AI algorithms.
-
+title: Train Classificator
+emoji: 😻
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: train a classification model using Scikit-Learn
 ---
 
-*Let's make AI accessible and easy to learn for everyone!*
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference