🧠 Computer Vision & Natural Language Processing

This repository showcases my work, assignments, and learning outcomes from the Computer Vision and Natural Language Processing module at Taylor’s University.

Throughout this course, I gained hands-on experience in Machine Learning, Deep Learning, Image Classification, Text Preprocessing, Tokenization, Neural Networks, and Real-World AI Applications.

The module covered both Computer Vision (CV) and Natural Language Processing (NLP) using modern AI techniques such as Convolutional Neural Networks (CNNs), text feature extraction, and understanding of neural network architectures.

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🚀 What I Learned

🖼 Computer Vision (CV)

• Image classification using Convolutional Neural Networks (CNNs)
• Understanding CNN components:
  • Convolutional layers (filters, kernels)
  • Max Pooling layers
  • Fully Connected Dense layers
  • Dropout regularization
  • Flatten layers
• Feature extraction
• Pixel-level operations & RGB channel processing
• Data preprocessing and resizing (128×128 images)
• Image augmentation: rotation, zoom, flip, shear
• Handling real-world image datasets

📚 Natural Language Processing (NLP)

• Text cleaning & preprocessing
• Word tokenization
• Stopword removal
• Stemming & Lemmatization
• Bag-of-Words & TF-IDF fundamentals
• Sequence modelling concepts
• Understanding how models read and interpret human language

🤖 Machine Learning & Deep Learning

• Neural Network fundamentals
• Activation functions: ReLU & Softmax
• Loss functions: Categorical Crossentropy
• Optimizers (Adam) with different learning rates
• Model evaluation:
  • Accuracy
  • Precision
  • Recall
  • F1-score
  • Confusion Matrix
• Early stopping
• Preventing overfitting

🛠 Tools & Technologies

• Python
• TensorFlow / Keras
• NumPy, OpenCV, Matplotlib
• Jupyter Notebook
• Kaggle Datasets
• CNN-based deep learning models
• NLP preprocessing tools

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🧠 Highlighted Project

📌 Meat Freshness Classifier Using Convolutional Neural Networks (CNN)

A fully implemented deep learning model that classifies meat into:

• Fresh
• Half-Fresh
• Spoiled

The full project is available in:

➡️ /Meat-Freshness-Classifier/

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This module strengthened my foundation in modern AI — allowing me to work with image data, understand neural architectures, and apply NLP techniques to real-world problems.