https://github.com/rameenmughal/ml-explorations
A space to analyze and experiment with Machine Learning algorithms, documenting the journey of learning in the world of AI and ML 🧭. This repository serves as a collection of notebooks, experiments, and insights.
https://github.com/rameenmughal/ml-explorations
dbscan google-colab jupyter-notebook kmedoids-clustering machine-learning machine-learning-algorithms python
Last synced: 3 months ago
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A space to analyze and experiment with Machine Learning algorithms, documenting the journey of learning in the world of AI and ML 🧭. This repository serves as a collection of notebooks, experiments, and insights.
- Host: GitHub
- URL: https://github.com/rameenmughal/ml-explorations
- Owner: RameenMughal
- Created: 2025-09-28T09:04:22.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-28T09:31:51.000Z (9 months ago)
- Last Synced: 2025-09-28T11:26:34.629Z (9 months ago)
- Topics: dbscan, google-colab, jupyter-notebook, kmedoids-clustering, machine-learning, machine-learning-algorithms, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.29 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Explorations
A space to analyze and experiment with Machine Learning algorithms, documenting the journey of learning in the world of AI and ML 🧭.
This repository serves as a collection of notebooks, experiments, and insights. The purpose is both to strengthen understanding and to provide a reference for students and enthusiasts exploring this field.
Currently, all studies and research are conducted using **Google Colab**, making the notebooks easy to run and share.
The long-term aim is to build a strong foundation in Machine Learning and apply this knowledge effectively in the field of Cybersecurity.
🔍 What’s inside:
- Hands-on notebooks covering ML algorithms and techniques
- Comparisons and analyses (e.g., K-Medoids vs. DBSCAN for clustering)
- Notes, reflections, and research explorations
💡 Collaboration & Feedback:
Mistakes or suggestions can be shared in the Issues tab. Contributions and discussions are always welcome!