Machine Learning Notebooks

About Me

Hi, I'm Aaron, a ML enthusiast. I began my ML journey a year ago. While I was immediately captivated by its power, I also realized how difficult it is for beginners to truly grasp the concepts, especially the underlying intuition and mathematical foundations. Despite the abundance of resources, many are overly complex, overly simplistic, or scattered across different platforms, and a unified, structured path that can guide someone from the ground up to mastery is often missing.

That's why I created this portfolio: as a curated record of my learning to break down ML topics with both intuitive insights and in-depth mathematical explanations. I believe to truly master ML, one needs to understand not just how things work, but why they work from both a intuitional and theoretical lens. My goal is to make complex topics more accessible with clear, structured, and intuitive explanations. These notebooks will help you build a strong foundation, but they are theory-heavy, which requires strong mathematical backgrounds. Beyond that, I highly encourage implementing the ideas to deepen your understanding. This is an ongoing, open-source project, and I welcome contributions from anyone to help expand and refine these resources. I hope this portfolio serves as a helpful companion on your journey.

Note: These notebooks haven't been professionally reviewed, so errors may be present. Please read them with a critical eye. I'm continuously learning and welcome any feedback or suggestions. If you spot a mistake or have ideas for new content, feel free to reach out or contribute via GitHub.