Machine Learning A-to-Z

This repository documents a complete, hands-on journey through machine learning.
It includes small, focused projects, mathematical derivations, experiments, and practice notebooks.
The goal is to build a strong foundation in both the practical and theoretical sides of machine learning.

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Repository Structure

ML-A-to-Z/
│
├── projects/
│   ├── classification/
│   │   ├── churn_prediction/
|   |   ├── crdit_card_detection/
│   │   ├── heart_disease_prediction/
|   |   ├── iris_classification/
|   |   ├── mnist_classfication/
│   │   ├── spam_detection/
|   |   └── titanic_survivors/
│   │
│   └── linear_regression/
│       ├── bike_sharing/
|       ├── car_price_predition/
│       └── house_price_prediction/
│
├── math/
│   └── linear_regression/
│       ├── normal_equation/
│       └── SGD_and_BGD/
│
├── experiments/
│   (scratch work, quick tests, temporary notebooks)
│
└── README.md

Folder meanings:

• projects/ -- complete ML projects organized by type (classification, regression).\
• math/ -- math notes and derivations for algorithms such as gradient descent and the normal equation.\
• experiments/ -- quick tests, scratch notebooks, and exploratory files.\
• each project contains:
  • data/ --- datasets\
  • notebooks/ --- Jupyter notebooks for EDA and model building\
  • optional src/ for .py scripts

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Skills and Tools Used

• Python\
• NumPy, pandas, Matplotlib\
• scikit-learn\
• Linear regression and classification models\
• Feature engineering and preprocessing\
• Exploratory Data Analysis (EDA)\
• Gradient descent and related optimization concepts\
• Git and reproducible project structure

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How to Use This Repository

1. Navigate to any project under projects/.\
2. Open the notebooks/ folder.\
3. Run the main notebook from top to bottom.\
4. Ensure datasets are inside the project's data/ folder.

Each project is self-contained and can be run independently.

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Purpose of This Repository

This repository tracks ongoing progress in machine learning, combining theory and implementation.
It serves as a learning log, a portfolio of completed work, and a foundation for more advanced topics.

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Future Additions

• Logistic regression math notes\
• Regularization methods (Ridge & Lasso)\
• Decision trees and ensemble models\
• Feature selection techniques\
• Time-series analysis\
• Introductory deep learning\
• Deployment examples