AI For Beginners

AI For Beginners is a comprehensive, open-source curriculum developed by Microsoft that guides learners through Artificial Intelligence over 12 weeks and 24 lessons. It covers foundational AI concepts from symbolic AI and knowledge representation to deep learning, computer vision, natural language processing, and AI ethics. Each lesson includes pre-reading material, executable Jupyter Notebooks in PyTorch and/or TensorFlow, quizzes, and hands-on labs. The curriculum is beginner-friendly, freely available on GitHub under the MIT License, and supported by a multilingual community.

• 24-Lesson Structured Curriculum — Fork or clone the repository and follow the lesson sequence from Introduction to AI through Neural Networks, Computer Vision, NLP, and AI Ethics.
• Dual Framework Support (PyTorch & TensorFlow) — Each practical lesson provides notebooks in both PyTorch and TensorFlow so learners can choose their preferred framework.
• Hands-On Labs — Many lessons include dedicated lab exercises that let you apply concepts to real problems, reinforcing learning through practice.
• Beginner-Friendly Examples — A dedicated examples folder includes Hello AI World, Simple Neural Network, Image Classifier, and Text Sentiment notebooks for those brand new to AI.
• Quizzes — All lessons are paired with quizzes available in the repository's quiz-app folder or hosted online, with gradual localization support.
• Multi-Language Support — The curriculum is translated into 50+ languages via automated GitHub Actions, making it accessible to a global audience.
• MS Learn Integration — Select lessons link to Microsoft Learn modules for deeper exploration of Computer Vision, NLP, Generative AI, and Responsible AI topics.
• Community & Discord — Learners can join the Microsoft Foundry Discord server to network, ask questions, and get support from peers and experienced developers.
• Topics Covered — Symbolic AI, Neural Networks, CNNs, Transfer Learning, GANs, Autoencoders, RNNs, Transformers, BERT, LLMs, Genetic Algorithms, Deep Reinforcement Learning, Multi-Agent Systems, and AI Ethics.