This curriculum provides a comprehensive 12-week, 24-lesson introduction to Artificial Intelligence for beginners, covering foundational concepts, modern deep learning techniques, and AI ethics. It is designed for individuals new to AI, offering practical lessons, quizzes, and hands-on labs using popular frameworks like TensorFlow and PyTorch.

How It Works

The curriculum is structured into modules covering Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and other AI techniques like Genetic Algorithms and Multi-Agent Systems. Each lesson includes pre-reading material, executable Jupyter Notebooks (available in PyTorch or TensorFlow variants), and optional labs for practical application. It also links to Microsoft Learn modules for supplementary learning.

Quick Start & Requirements

• Install/Run: Fork and clone the repository (git clone https://github.com/microsoft/AI-For-Beginners.git). Follow setup instructions in the "Course Setup" lesson.
• Prerequisites: Python, Jupyter Notebooks, TensorFlow or PyTorch. Specific hardware requirements are not detailed but typical for ML development.
• Resources: Links to Microsoft Learn modules are provided throughout. A Discord server is available for community support.

Highlighted Details

• Covers both traditional Symbolic AI (GOFAI) and modern Deep Learning.
• Includes practical implementation guidance with PyTorch and TensorFlow.
• Features dedicated sections on Computer Vision and Natural Language Processing.
• Addresses AI Ethics and Responsible AI principles.

Maintenance & Community

• Primary Author: Dmitry Soshnikov, PhD. Editor: Jen Looper, PhD.
• Active community support via an official AI Discord server.
• Other related curricula are available from the same team.

Licensing & Compatibility

• The repository content is generally available under permissive licenses, but specific component licenses (e.g., for notebooks or quizzes) should be verified. Compatibility for commercial use is likely, but explicit confirmation is recommended.

Limitations & Caveats

The curriculum may be "a bit lacking in the state-of-the-art" for some neural architectures. It explicitly excludes deep mathematics, classic machine learning, specific cloud frameworks, conversational AI, and business case studies, directing users to other resources for these topics.