Overview
A structured curriculum defining the comprehensive set of knowledge and skills that modern AI professionals should possess. This project serves as a continuously updated knowledge repository, mapping out the landscape of AI expertise from foundational concepts to cutting-edge practices.
Core Areas
- Foundational Mathematics: Linear algebra, probability, statistics, optimization
- Machine Learning: Supervised, unsupervised, and reinforcement learning
- Deep Learning: Neural architectures, training techniques, model optimization
- Natural Language Processing: Transformers, LLMs, prompt engineering
- MLOps & Production: Model deployment, monitoring, versioning, scaling
- AI Safety & Ethics: Alignment, bias mitigation, responsible AI practices
- Agent Systems: Autonomous agents, tool use, multi-agent coordination
Recognized Programs & Courses
This curriculum builds upon and references established AI education programs from leading experts:
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Andrew Ng’s Agentic AI Course (DeepLearning.AI, 2025): Focuses on four key agentic design patterns - reflection, tool use, planning, and multi-agent collaboration. Teaches production-ready agentic application development with emphasis on evaluation and error analysis.
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Yann LeCun’s Deep Learning Course (NYU): Comprehensive coverage of supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent networks. Curriculum includes optimization techniques, energy-based models, world models, and GANs.
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Geoffrey Hinton’s Neural Networks for Machine Learning (Coursera): Foundational course covering artificial neural networks, backpropagation, RNNs, optimization methods, and techniques for improving generalization.
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Fei-Fei Li’s CS231n (Stanford): Deep dive into convolutional neural networks and computer vision. Covers deep learning basics, visual perception and understanding, and generative and interactive visual intelligence.
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Andrej Karpathy’s Neural Networks: Zero to Hero: Hands-on course building neural networks from scratch, progressing from backpropagation basics to modern GPT architectures. Emphasizes practical implementation and understanding of internals.
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Yoshua Bengio’s Deep Learning Textbook: Co-authored with Ian Goodfellow and Aaron Courville, this comprehensive textbook serves as the foundational resource for deep learning theory and practice worldwide.
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Stanford & MIT AI Programs: Graduate-level curricula covering the full spectrum of AI, including machine learning, NLP, robotics, computer vision, and AI safety from institutions at the forefront of AI research.
Features
- Continuously updated with emerging AI developments
- Structured learning paths for different specializations
- Practical implementation examples and exercises
- Industry-relevant case studies and applications
- Resource curation from academic and industry sources
In Progress
- Developing automated content aggregation pipeline
- Building knowledge graph of AI concepts and dependencies
- Creating assessment framework for skill validation
- Integrating real-world project templates
- Adding community contribution mechanisms