📊 Data Mining & Advanced Topics

Advanced AI/ML topics including Data Mining, Neural Network Theory, and Comprehensive Image Processing demonstrating deep understanding of machine learning fundamentals and advanced techniques.

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📋 Table of Contents

• Projects Overview
• Technologies Used
• Installation
• Project Details
• Key Concepts
• Contact

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🚀 Projects Overview

#	Project	Category	Notebook	Focus
1	Data Mining Project	Data Science	01_data_mining_project.ipynb	Clustering, Association Rules
2	ANN Loss Functions	Deep Learning Theory	02_ann_loss_functions.ipynb	Softmax, Sigmoid, Cross-Entropy
3	Image Processing	Computer Vision	03_comprehensive_image_processing.ipynb	Complete CV Pipeline
4	ML Comprehensive Exam	Machine Learning	04_ml_comprehensive_exam.ipynb	End-to-End ML Tasks

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🛠️ Technologies Used

Data Mining

• scikit-learn - Clustering, classification
• Pandas - Data manipulation
• Association rule mining - Market basket analysis

Deep Learning

• TensorFlow/Keras - Neural networks
• Loss functions - Optimization theory
• Activation functions - Softmax, Sigmoid, ReLU

Image Processing

• OpenCV - Computer vision
• PIL/Pillow - Image manipulation
• NumPy - Array operations

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📦 Installation

git clone https://github.com/uzi-gpu/data-mining-advanced.git
cd data-mining-advanced
python -m venv venv
source venv/bin/activate  # Windows: venv\\Scripts\\activate
pip install -r requirements.txt
jupyter notebook

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📊 Project Details

1. 📈 Data Mining Project

File: 01_data_mining_project.ipynb

Objective: Apply data mining techniques to discover patterns in data

Techniques:

• Clustering: K-Means, Hierarchical
• Classification: Decision Trees, Random Forest
• Association Rules: Apriori algorithm
• Pattern Discovery: Frequent itemsets

Applications:

• Customer segmentation
• Market basket analysis
• Anomaly detection
• Recommendation systems

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2. 🧠 ANN Loss Functions

File: 02_ann_loss_functions.ipynb

Objective: Deep dive into neural network loss functions and optimization

Loss Functions Covered:

1. Binary Cross-Entropy:

BCE = -[y*log(ŷ) + (1-y)*log(1-ŷ)]

• Use case: Binary classification
• Range: [0, ∞)

2. Categorical Cross-Entropy:

CCE = -Σ(y_i * log(ŷ_i))

• Use case: Multi-class classification
• Requires: One-hot encoded labels

3. Mean Squared Error (MSE):

MSE = (1/n) * Σ(y - ŷ)²

• Use case: Regression
• Sensitive to outliers

Activation Functions:

• Sigmoid: σ(x) = 1/(1+e^(-x))
• Softmax: e^(x_i) / Σe^(x_j)
• ReLU: max(0, x)

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3. 🖼️ Comprehensive Image Processing

File: 03_comprehensive_image_processing.ipynb

Objective: Complete image processing pipeline from basics to advanced

Topics Covered:

Fundamentals:

• Image loading and display
• Color space conversions
• Image resizing and cropping

Filtering:

• Gaussian blur
• Median filtering
• Bilateral filter
• Sharpening

Edge Detection:

• Canny edge detector
• Sobel operator
• Laplacian

Morphological Operations:

• Erosion and dilation
• Opening and closing
• Morphological gradient

Advanced:

• Histogram equalization
• Image transforms (FFT)
• Feature detection (corners, blobs)
• Image segmentation

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4. 🎯 ML Comprehensive Exam

File: 04_ml_comprehensive_exam.ipynb

Objective: Demonstrate comprehensive ML knowledge

Skills Demonstrated:

• Data preprocessing
• Model selection
• Hyperparameter tuning
• Cross-validation
• Performance evaluation
• Feature engineering

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📚 Key Concepts Demonstrated

Data Mining

1. Unsupervised Learning - Clustering without labels
2. Association Rules - Mining relationships
3. Pattern Discovery - Finding hidden insights
4. Dimensionality Reduction - PCA, t-SNE

Deep Learning Theory

1. Loss Functions - Optimization objectives
2. Backpropagation - Gradient computation
3. Activation Functions - Non-linearity
4. Optimization - SGD, Adam, RMSprop

Image Processing

1. Spatial Domain - Direct pixel manipulation
2. Frequency Domain - FFT transformations
3. Feature Extraction - Corners, edges, textures
4. Image Enhancement - Filters, equalization

Machine Learning

1. Model Evaluation - Accuracy, precision, recall
2. Cross-Validation - K-fold validation
3. Ensemble Methods - Bagging, boosting
4. Feature Selection - Important variable identification

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🎓 Learning Outcomes

This repository demonstrates:

1. Data Mining Expertise

   • Clustering algorithms
   • Association rule mining
   • Pattern discovery
   • Practical applications

2. Deep Learning Theory

   • Loss function mathematics
   • Optimization principles
   • Activation function analysis
   • Training dynamics

3. Image Processing

   • Complete CV pipeline
   • Filter design and application
   • Feature extraction
   • Advanced techniques

4. ML Proficiency

   • End-to-end pipelines
   • Model evaluation
   • Best practices
   • Production readiness

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📧 Contact

Uzair Mubasher - BSAI Graduate

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📄 License

MIT License - see LICENSE

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