This repository offers a comprehensive collection of machine learning examples and tutorials, primarily aimed at individuals learning or practicing data science and AI. It provides code snippets and project structures to accompany various online courses, facilitating hands-on learning of diverse ML techniques.

How It Works

The repository organizes code by course, with each folder representing a distinct learning module. This structure allows users to easily locate and access the specific code relevant to their studies. The project emphasizes frequent git pull operations to stay updated with course material, recommending cloning over forking to avoid outdated code.

Quick Start & Requirements

• Install: Clone the repository using git clone.
• Prerequisites: Python, along with libraries specific to each course's examples (e.g., TensorFlow, PyTorch, NumPy). Specific versions are not mandated in the README.
• Resources: Requires standard development environment setup.
• Documentation: Associated tutorials and course links are available at https://lazyprogrammer.me and https://deeplearningcourses.com.

Highlighted Details

• Extensive coverage of various ML domains including NLP, computer vision, time series analysis, and reinforcement learning.
• Examples span multiple popular frameworks like TensorFlow 2.0 and PyTorch.
• Includes foundational math courses for data science (Linear Algebra, Calculus, Probability).
• Features advanced topics like Generative AI, DeepFakes, and financial AI.

Maintenance & Community

The repository is maintained by "lazyprogrammer," with numerous course links provided, indicating active development and a strong tie to educational content. No specific community channels (Discord, Slack) or other contributors are mentioned.

Licensing & Compatibility

The repository's license is not explicitly stated in the README.

Limitations & Caveats

The README explicitly warns against forking due to frequent updates, suggesting cloning instead. Some newer code examples, particularly for TensorFlow 2.0, are hosted on Google Colab and may not be directly present in the repository. Users are advised to consult course lectures for precise code locations.