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7-Day, 7-Project Machine Learning & Deep Learning Portfolio

Welcome to my 7-Day, 7-Project Machine Learning & Deep Learning challenge. Over the course of 7 days, I developed seven different projects to build my portfolio and dive deep into various AI/ML techniques across different domains. Each project tackles real-world problems using advanced machine learning and deep learning models.

Table of Contents

1. Day 1: Image Classification using CNNs
2. Day 2: Cryptographic Algorithm Detection

Project Descriptions

Day 1: Image Classification using CNNs

• Description: Developed a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset into 10 different categories.
• Technologies: TensorFlow, Keras, PyTorch
• Dataset: CIFAR-10
• Highlights: Demonstrates basic image classification using CNNs, data preprocessing, and augmentation techniques.

Day 2: Cryptographic Algorithm Detection

• Description: Leveraged AI/ML techniques to identify and classify various cryptographic algorithms from a custom dataset.
• Technologies: TensorFlow, Scikit-learn, PyCryptodome
• Dataset: Custom-generated cryptography dataset
• Highlights: Implemented feature extraction and classification models for accurate cryptographic algorithm detection.

Conclusion

These seven projects were designed to cover a wide range of machine learning and deep learning techniques, from image classification and medical imaging to natural language processing and time series forecasting. By completing these, I aimed to strengthen my understanding of real-world applications and improve my skills as an ML/DL developer.

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