🧠 AI Learning & Experimentation Space

This repository documents my learning path, experiments, and hands-on experience in Artificial Intelligence and Machine Learning.

The work here is primarily driven by studying well-known books and actively experimenting with their ideas through code. Rather than treating the material as static examples, I use it as a foundation for exploration, modification, and deeper understanding.

Each top-level folder in this repository is named after a book and contains chapter-based implementations and experiments.

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📁 Repository Structure

• Each folder name corresponds to a book
• Inside each book folder:
  • Chapters are organized as ch01, ch02, ch03, ...
  • Each chapter typically contains:
    • A Python source file (.py)
    • A Jupyter Notebook (.ipynb)

📌 For readers and experimenters, Jupyter Notebook files are recommended.
The original development, however, was mostly done using Python files inside a code editor.

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📚 Books Covered

Machine Learning with Scikit-Learn, Keras & PyTorch

Authors: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

This book provides a practical and structured introduction to machine learning, progressing from fundamental concepts to modern deep learning workflows.

High-level chapter overview:

• Introduction to machine learning concepts, data preprocessing, and evaluation techniques
• Classical machine learning algorithms such as linear models, decision trees, ensemble methods, and dimensionality reduction
• Neural networks and deep learning using PyTorch, including training loops and optimization
• Strong emphasis on practical implementation alongside theory

Repository layout for this book:

• Chapters are stored as:
  • ch01, ch02, ch03, ...
• Each chapter folder contains:
  • A .py file with the main implementation
  • A corresponding .ipynb notebook for interactive use

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🛠 Development Workflow

Although Jupyter Notebooks are included for accessibility, I personally prefer writing code in Neovim.

To maintain an interactive workflow while coding in .py files:

• I used pywork.nvim to run Python cells dynamically inside Neovim
  🔗 https://github.com/jeryldev/pyworks.nvim
• I used jupytext to convert and synchronize .py files with Jupyter Notebooks

This setup enables:

• Cleaner, version-control-friendly Python code
• Notebook compatibility across different platforms
• A flexible workflow for both script-based and notebook-based users

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📝 Notes

This repository is a living workspace.
Code may include experiments, deviations from the book, or exploratory implementations, reflecting learning-in-progress rather than production-ready libraries.

Feel free to explore, experiment, and adapt the material 🚀