This repository provides a curated collection of free resources for learning machine learning with Python and Jupyter Notebooks, targeting individuals new to ML or those interested in its ethical implications. It offers a hands-on, learn-by-doing approach with links to notebooks, courses, and foundational concepts, aiming to make ML accessible and understandable.

How It Works

The project acts as a comprehensive guide, linking to external resources like Jupyter Notebooks, online courses (e.g., Andrew Ng's Machine Learning), and foundational articles. It emphasizes practical application through coding exercises and provides context on ML terminology, ethical considerations, and the broader data science landscape. The approach is to aggregate and organize high-quality, free learning materials, allowing users to follow a structured path or explore specific topics.

Quick Start & Requirements

• Installation: Primarily relies on local Python environments (Anaconda recommended for ease of package management) or cloud-based platforms like Google Colab and Binder.
• Prerequisites: Python 3, Jupyter Notebook, and core scientific libraries (numpy, pandas, scikit-learn, matplotlib).
• Resources: Links to official documentation, tutorials, and interactive notebooks are provided throughout.

Highlighted Details

• Curated list of free online courses and textbooks for ML and Deep Learning.
• Emphasis on ethical ML principles and responsible AI practices.
• Links to numerous Jupyter Notebooks for hands-on experimentation.
• Guidance on practice studies, competitions, and collaborating with domain experts.

Maintenance & Community

The project was first posted in 2016 and appears to be a static collection of curated links. Community interaction is encouraged via pull requests for new resource suggestions. Relevant subreddits like /r/LearnMachineLearning and /r/MachineLearning are mentioned for community support.

Licensing & Compatibility

The repository itself does not host code that requires a specific license; it is a collection of links to external resources, which may have their own licenses. Compatibility is broad, supporting standard Python environments and cloud platforms.

Limitations & Caveats

As a curated list, the project's content is dependent on the longevity and accessibility of the linked external resources. The information may not always reflect the absolute latest advancements in the rapidly evolving field of machine learning.