🤖 Machine Learning & AI Codebase (ml-learning)

Comprehensive, hands-on repository covering Supervised Learning, Unsupervised Learning, Reinforcement Learning (Q-Learning), Computer Vision (OpenCV & YOLOv8), PyTorch Deep Learning, Pandas Data Science, and TensorFlow Fundamentals.

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📌 Repository Modules & Highlights

1. 📈 Supervised Learning (supervised_practice/ & revision/)

• Regression Models: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Ridge, Lasso, and ElasticNet Regularization.
• Classification Models: Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM).
• Real-World Case Studies:
  • disease_prediction.py: Multiclass health diagnostic predictor.
  • mail_detection.py: Spam vs. Ham email classifier.
  • temperature_forecasting.py: Time-series temperature predictor.
  • house_price_prediction.py: Real estate valuation pipeline.

2. 🧩 Unsupervised Learning (unsupervised_practice/ & unsupervised/)

• Clustering Algorithms:
  • K-Means Clustering & Elbow Method for optimal cluster evaluation ($k$).
  • Agglomerative Hierarchical Clustering with Dendrogram analysis.
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
• Dimensionality Reduction:
  • Principal Component Analysis (PCA) & Linear Discriminant Analysis (LDA).
• Association Rule Learning:
  • Apriori algorithm for market basket analysis.

3. 🎮 Reinforcement Learning (reinforcement_learning/)

• Q-Learning Algorithm: Tabular Q-learning with Epsilon-Greedy exploration/exploitation decay.
• Environment Implementations:
  • GridWorld Navigation: Agent finding optimal path to bottom-right destination.
  • Traffic Light Signal Optimization: Q-value based signal switching to reduce congestion.

4. 👁️ OpenCV & Object Detection (opencv/)

• OpenCV Basics: Image transformations, color spaces, blurring, thresholding, edge detection, and contour analysis.
• YOLOv8 & Object Tracking:
  • 01-yolo_basics.py: YOLOv8 object detection on static images.
  • 02-yolo_webcam.py: Real-time camera feed object detection.
  • 04-person_tracking.py & 06-bytetrack_detection.py: Multi-object tracking with ByteTrack.
  • 09-entry_exit_counter.py: Line-crossing entry & exit analytics counter.

5. 🧠 PyTorch Deep Learning (pytorch_01/ & pytorch02/)

• Neural Network Architecture: Custom nn.Module classes, linear layers, activation functions (ReLU, Sigmoid, Softmax).
• Convolutional Neural Networks (CNNs):
  • Custom CNNs, ResNet architectures, Residual Blocks (Skip Connections), Stride & Padding mechanics.
  • MNIST Handwritten Digit Classification.

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📁 Directory Structure

ml-learning/
├── supervised_practice/       # Linear/Logistic Regression, Decision Trees, KNN, SVM
├── unsupervised_practice/     # K-Means, Elbow Method, PCA, LDA, DBSCAN, Apriori
├── reinforcement_learning/    # Q-Learning, Epsilon-Greedy, Traffic & GridWorld
├── opencv/                    # OpenCV Filters, YOLOv8 Detection, ByteTrack Counter
├── pytorch_01/                # PyTorch Tensors, Autograd, Convolutions
├── pytorch02/                 # ResNet, Residual Blocks, MNIST CNN Classifier
├── pandas_basics/             # Data Frames, Missing Value Imputation, Selection
└── tensorflow/                # Neural Network Fundamentals

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🛠️ Environment Setup & Installation

# Clone the repository
git clone https://github.com/dipesh097/ml-learning.git
cd ml-learning

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install core packages
pip install numpy pandas matplotlib scikit-learn opencv-python torch torchvision ultralytics

Run any script:

python supervised_practice/"Disease Prediction.py"