Welcome to my Machine Learning & Deep Learning repository! This space serves as a personal collection of practical implementations, solutions to exercises, and projects covering a wide range of ML and DL topics.
This repository is a log of my journey through AI, intended for learning, practice, and reference. You'll find implementations of classic algorithms (sometimes from scratch) as well as applications using popular libraries.
This collection includes a variety of topics, from the fundamentals of data preprocessing and simple linear regression to more complex neural network architectures for computer vision and NLP.
The main goals of this repository are to:
- Solve practical exercises from courses, books, and real-world problems.
- Build a portfolio of hands-on data science projects.
- Serve as a "cookbook" for common ML/DL tasks and code snippets.
The exercises and projects in this repository primarily use the following technologies:
- Language: Python 3.12+
- Core Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Machine Learning: Scikit-learn
- Deep Learning: TensorFlow 2.x (You will need to learn WSL2 to run it on windows with GPU support), Keras, PyTorch
- Environment: Jupyter Notebooks (.ipynb) & Python Scripts (.py)
This project is licensed under the Creative Commons.