Welcome to the Deep Learning and Machine Learning Models Repository! This collection features a diverse set of models, including classical machine learning algorithms and state-of-the-art deep learning architectures. The repository is organized into two main sections:
- Linear Regression: Predictive modeling technique used for regression analysis.
- Logistic Regression: Classification algorithm for binary classification problems.
- Random Forest: Ensemble learning method for classification, regression, and other tasks.
- XGBoostRegressor: Gradient boosting framework known for its speed and performance.
- Decision Tree Classifier: Tree-like model used for classification.
- Convolutional Neural Network (CNN): Deep learning architecture designed for image and visual data processing.
- Artificial Neural Network (ANN): General-purpose deep learning architecture for various tasks.
- Plant Disease Classification: Utilizing CNNs for identifying plant diseases from images.
- Food Classification: Applying deep learning to classify different types of food items.
- Handwritten Digit Classification: Using neural networks to recognize and classify handwritten digits.
- Clone the repository:
git clone https://github.com/Raahim2/Machine-Learning.git - Explore the Machine Learning Algorithms or Deep Learning Models directory based on your interest.
- Refer to the documentation within each model's directory for usage instructions and examples.
Contributions are highly encouraged! Whether you want to add new models, fix bugs, or enhance existing implementations, please open an issue or submit a pull request.
This repository is licensed under the MIT License.
- A big thank you to the open-source community for their continuous contributions.
- Special appreciation for contributors who help make this repository a valuable resource for the community.
Happy coding!