[WIP] Add additional Jupyter notebook templates for ML use cases

[Copilot]

Thanks for asking me to work on this. I will get started on it and keep this PR's description up to date as I form a plan and make progress.

Original prompt

Expand the existing Jupyter notebook templates in the MLLanguageModels repository to cover additional machine learning and deep learning use cases based on industry standards. Specifically, add templates for the following:

1. Clustering Models: A notebook template for implementing unsupervised clustering algorithms (e.g., K-Means, DBSCAN, or Hierarchical Clustering).
2. Reinforcement Learning: A template to demonstrate basic reinforcement learning concepts using frameworks like Stable-Baselines3 or OpenAI Gym.
3. Anomaly Detection: A notebook for anomaly detection methods, including Isolation Forest, Autoencoders, or One-Class SVM.
4. Time Series Analysis: A template for forecasting and analyzing time-series data using models like ARIMA, LSTM, or Prophet.
5. Computer Vision Models: A notebook for image classification or object detection using convolutional neural networks (CNN) or pre-trained models like YOLO or Faster R-CNN.

Ensure each template includes documentation, example datasets, and instructions for setup and execution.

This pull request was created as a result of the following prompt from Copilot chat.

Expand the existing Jupyter notebook templates in the MLLanguageModels repository to cover additional machine learning and deep learning use cases based on industry standards. Specifically, add templates for the following:

1. Clustering Models: A notebook template for implementing unsupervised clustering algorithms (e.g., K-Means, DBSCAN, or Hierarchical Clustering).
2. Reinforcement Learning: A template to demonstrate basic reinforcement learning concepts using frameworks like Stable-Baselines3 or OpenAI Gym.
3. Anomaly Detection: A notebook for anomaly detection methods, including Isolation Forest, Autoencoders, or One-Class SVM.
4. Time Series Analysis: A template for forecasting and analyzing time-series data using models like ARIMA, LSTM, or Prophet.
5. Computer Vision Models: A notebook for image classification or object detection using convolutional neural networks (CNN) or pre-trained models like YOLO or Faster R-CNN.

Ensure each template includes documentation, example datasets, and instructions for setup and execution.

---

💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

[Copilot]

Copilot wasn't able to review any files in this pull request.

---

_{Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.}