This selection of notebooks is designed to help learners and practitioners to get hands-on experience with machine learning concepts using Python. Whether you are just starting to explore machine learning or seeking to deepen your practical skills, this repository provides a structured approach that covers data preprocessing, model building, evaluation, along with essential ML theory.
The Jupyter notebooks will walk you through real-world examples using popular libraries such as scikit-learn, Pandas and NumPy. Each module focuses on key aspects of machine learning including regression, classification, unsupervised learning and building end-to-end pipelines. Supplementary materials such as lecture slides and sample datasets are included to enhance the learning experience and help you apply concepts directly to projects.
To get started, simply clone the repository and install the dependencies listed in the requirements file. The course materials are organized for sequential learning, but you’re welcome to navigate the topics that interest you most. Contributions and feedback are encouraged to help us improve and expand these resources for the wider data science community.