A structured repository documenting my journey of mastering the mathematical foundations required for Machine Learning, Artificial Intelligence, and Computer Science.
This repository contains organized lecture notes, visual explanations, examples, and structured learning paths across core mathematical subjects.
| Subject | Description | Folder Link |
|---|---|---|
| Statistics | Probability, distributions, hypothesis testing, statistical inference | Statistics |
| Linear Algebra | Vectors, matrices, eigenvalues, linear transformations | Linear Algebra |
| Calculus | Derivatives, integrals, limits, optimization | Calculus |
| Probability | Random variables, probability theory, expectation | Probability |
More subjects will be added as the learning journey continues.
Build strong mathematical intuition for ML and AI
Maintain structured and revision friendly notes
Create a long term mathematical reference library
Document progress publicly for accountability
mathematics-foundations/
│
├── Statistics/
├── Linear-Algebra/
├── Calculus/
├── Probability/
└── README.md
Each subject folder contains
📖 Lecture wise notes
🧠 Concept explanations with examples
🖼️ Supporting diagrams and screenshots
🔗 External resources when applicable
Navigate to a subject folder
Open lecture folders in sequence
Use the notes for revision and conceptual clarity
This repository grows continuously as I progress deeper into mathematical theory and AI applications.
To build a strong theoretical foundation that supports
Advanced Machine Learning
Deep Learning
Research oriented AI work
Mathematical maturity for problem solving
If you would like to connect, collaborate, or provide feedback:
Email: aqibjaved5201@gmail.com
GitHub: https://github.com/AqibNiazi
LinkedIn: https://www.linkedin.com/in/maqibjaved/
Feel free to reach out regarding statistics, machine learning, or academic discussions.