https://github.com/mugisha-pascal/machine-learning

A variety of projects expressing my full journey in machine learning and deep learning using python and jupyter notebook for documentation
https://github.com/mugisha-pascal/machine-learning

joblib machine-learning matplotlib pandas sklearn tensorflow

Last synced: 4 months ago
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A variety of projects expressing my full journey in machine learning and deep learning using python and jupyter notebook for documentation

• Host: GitHub
• URL: https://github.com/mugisha-pascal/machine-learning
• Owner: MUGISHA-Pascal
• Created: 2024-07-08T12:46:12.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2026-02-03T09:15:40.000Z (5 months ago)
• Last Synced: 2026-02-03T20:31:14.422Z (5 months ago)
• Topics: joblib, machine-learning, matplotlib, pandas, sklearn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 12.6 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Machine Learning Repository

A comprehensive collection of machine learning projects, tutorials, experiments, and learning sessions covering various ML algorithms, datasets, and real-world applications.

## Repository Structure

```

.

├── notebooks/              # Jupyter notebooks organized by purpose

│   ├── tutorials/         # Learning materials and algorithm implementations

│   │   ├── algorithms/    # Linear regression, MNIST, and other algorithms

│   │   ├── backpropagation/

│   │   ├── computer-vision/

│   │   └── convolutional-neural-network/

│   ├── examples/          # Dataset examples and demonstrations

│   │   ├── breastCancer.ipynb

│   │   ├── CaliforniaHousing.ipynb

│   │   └── SVM(irisdataset).ipynb

│   ├── experiments/       # Test notebooks and custom implementations

│   │   ├── CSV_to_dataset_keras.ipynb

│   │   └── Keras_custom_model.ipynb

│   └── visualization/     # Data visualization notebooks and resources

│       └── data_visualization.ipynb

├── projects/              # Production-ready ML projects with Flask APIs

│   ├── breast-cancer-project/

│   ├── california-housing-project/

│   ├── diabetes-project/

│   ├── irisFeature-project/

│   ├── music-genre-generation-project/

│   ├── student-grade-project/

│   ├── student_performance_index/

│   ├── videoGame-project/

│   └── wine-project/

└── sessions/              # Learning sessions and practice work

    ├── 02-02-2026/

    ├── 19-01-2026/

    └── 22-01-2026_Classification/

```

## Getting Started

1. **Learning**: Navigate to `notebooks/tutorials/` for algorithm implementations and learning materials

2. **Examples**: Check `notebooks/examples/` for dataset-specific demonstrations (Breast Cancer, California Housing, Iris SVM)

3. **Experiments**: Explore `notebooks/experiments/` for custom Keras models and data processing techniques

4. **Projects**: Browse `projects/` for complete ML applications with APIs and demos

5. **Sessions**: Review `sessions/` for dated learning sessions and classification work

## Projects

Each project folder typically contains:

- **Training scripts** (`train.py`) - Model training and evaluation

- **Flask API** (`app.py`) - REST API for model predictions

- **Trained models** (`model/`) - Serialized model files

- **Demo applications** (`demo/`, `nodeApp/`) - Frontend interfaces for testing

### Available Projects

- **Breast Cancer Detection** - Classification model for cancer diagnosis

- **California Housing** - Regression model for housing price prediction

- **Diabetes Prediction** - Healthcare prediction model

- **Iris Feature Classification** - Classic iris dataset classification

- **Music Genre Generation** - Audio/music classification

- **Student Grade Prediction** - Educational performance prediction

- **Student Performance Index** - Academic performance analysis

- **Video Game Analysis** - Gaming data analysis

- **Wine Quality** - Wine classification/regression

## Notebooks

### Tutorials

Comprehensive learning materials covering:

- Algorithm implementations (linear regression, neural networks, etc.)

- Backpropagation fundamentals

- Computer vision techniques

- Convolutional neural networks (CNNs)

### Examples

Real-world dataset implementations:

- Breast cancer classification using various algorithms

- California housing price prediction

- Support Vector Machines (SVM) on iris dataset

### Experiments

Custom implementations and explorations:

- CSV to Keras dataset conversion

- Custom Keras model architectures

### Visualization

Data analysis and visualization techniques for ML datasets

## Sessions

Dated learning sessions containing practice work, experiments, and specific topic explorations (e.g., classification techniques, recommendation systems)