🧠 Machine Learning Portfolio

A growing collection of ML models built to learn core machine-learning fundamentals and industry-ready workflows.

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📌 Overview

This repository serves as my Machine Learning learning hub, where I build and store complete, end-to-end ML projects.

Each project lives inside the ML-Models/ directory and includes:

• Clear problem definition
• Full preprocessing pipeline
• Model building (MLP, CNN, etc.)
• Training + evaluation
• Confusion matrix & error analysis
• Final results
• A clean, professional README

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📁 Repository Structure

Machine-Learning-Portfolio/
│
├── ML-Models/
│   ├── MNIST-Digit-Classifier-MLP-CNN/
│   │   ├── mnist-digit-classifier-model.ipynb
│   │   ├── README.md
│   │   └── thumbnail.png  (optional)
│   │
│   ├── (Future models…)
│
└── LICENSE
└── README.md

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🚀 Completed Models

1. MNIST Digit Classifier (MLP → CNN)

A full machine-learning project built on Kaggle using:

• Baseline MLP
• Deep CNN
• 99.3% accuracy
• Confusion matrices
• Error visualizations

📂 Folder: /ML-Models/MNIST-Digit-Classifier-MLP-CNN/
📓 Includes Kaggle notebook + detailed README.

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📌 Future Models Planned

• CIFAR-10 Image Classifier (CNN + augmentations)
• Sentiment Analysis with RNN/BERT
• Spam Detection (NLP)
• Tabular ML models (RandomForest, XGBoost)
• FashionMNIST CNN
• Regression models (housing price prediction)

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📜 License

This project is licensed under the MIT License, which allows personal and commercial use.

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⭐ Support

If you like this portfolio, consider starring the repo — it helps a lot!