README

# Artificial Intelligence Projects

Welcome to my AI and Machine Learning repository! This collection features projects that explore various AI and ML techniques, ranging from Bayesian Networks to Neural Networks, with real-world applications in classification, detection, and optimization.

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## **Overview**
This repository contains a mix of coursework assignments, personal projects, and experimental implementations. Two main projects to check out:
- **[Covid-Risk Bayesian Network](path-to-folder):** A probabilistic model for assessing risk during the pandemic.
- **[Sarcasm Detection Neural Network](path-to-folder):** A deep learning model for classifying text as sarcastic or non-sarcastic.

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## **Contents**
| Folder Name                     | Description                                                                                      |
|---------------------------------|--------------------------------------------------------------------------------------------------|
| `alarm-bayesian-network`        | A Bayesian Network implementation for alarm systems.                                             |
| `classifiers`                   | Various classification algorithms implemented and compared.                                      |
| `cnn-from-scratch`              | Convolutional Neural Network implemented without external libraries.                             |
| `flann-based-matcher`           | Feature matching using FLANN for image processing tasks.                                         |
| `hate-speech-detection`         | Neural network to classify tweets as hate speech or not.                                         |
| `machine-learning-google-colab`| Jupyter notebooks showcasing ML experiments using Google Colab.                                  |
| `monty-hall-bayesian-network`   | A probabilistic model simulating the Monty Hall problem.                                         |
| `resnet`                        | Implementation and fine-tuning of ResNet architectures for image classification tasks.          |
| `sarcasm-detection`             | A neural network project focused on sarcasm detection in text.                                  |
| `spam-detection`                | Spam email classifier using text preprocessing and machine learning.                            |
| `wet-grass-bayesian-network`    | A Bayesian Network project modeling environmental conditions.                                   |
| `wine-classifier`               | Classification of wine quality based on physicochemical properties using ML algorithms.         |
| `yolov11`                       | YOLO-based object detection for real-time applications.                                         |

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## **Getting Started**
To explore these projects:
1. Clone the repository:
   ```bash
   git clone https://github.com/roscoekerby/artificial-intelligence.git

2. Navigate to the project folder of interest.
3. Follow the README file or notebook in each directory for detailed instructions.

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Key Features

• Bayesian Networks: Real-world probabilistic modeling for decision-making under uncertainty.
• Deep Learning Models: Includes CNNs, ResNet architectures, and NLP models.
• Machine Learning Experiments: Implemented with scikit-learn, TensorFlow, and PyTorch.
• Real-World Applications: Projects address problems in image processing, natural language processing, and risk assessment.

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Requirements

• Python 3.x
• Jupyter Notebook
• TensorFlow / PyTorch
• Libraries: numpy, pandas, matplotlib, scikit-learn, etc.

For project-specific dependencies, refer to the requirements.txt in the respective directories.

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License

This repository is open-sourced under the MIT License. See the LICENSE file for details.

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Contact

For questions or collaboration, feel free to connect:

• GitHub: roscoekerby
• LinkedIn: Roscoe Kerby
• Medium: Roscoe Kerby

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Happy coding!