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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7822 Paths
7822 Paths · page 780 / 783
An interdisciplinary learning path that bridges foundational mathematics, physics, chemistry, and biology with modern machine learning techniques. This path equips STEM researchers and AI engineers with the skills to apply AI as a tool for scientific discovery, covering numerical methods, physics-informed neural networks, and domain-specific applications in molecular science, materials, and drug discovery.
A comprehensive learning path that guides you from classical NLP fundamentals through modern Large Language Models to building advanced AI Agent applications. Focuses on conceptual understanding, implementation principles, and production considerations.
A comprehensive, prerequisite-driven learning path that builds the essential mathematical foundations for artificial intelligence and machine learning, from arithmetic to deep learning theory.
A comprehensive 70-node learning path from foundational mathematics and programming to designing, building, evaluating, and deploying production-grade AI systems, covering classical ML, deep learning, NLP, computer vision, LLMs, RAG, agents, and MLOps with a strong emphasis on practical implementation and real-world projects.
A comprehensive learning path designed to transition a learner with a background in computational chemistry into an AI Drug Discovery Scientist. This path integrates core chemistry principles with modern AI and biotechnology applications.
This learning path bridges classical physics simulation techniques with modern artificial intelligence approaches. Starting from fundamental physics concepts and numerical methods, the journey progresses through traditional simulation paradigms to machine learning applications, culminating in hybrid AI-physics models for scientific discovery.
A comprehensive learning path bridging essential mathematics, programming, and data science fundamentals toward advanced AI research and development competencies for aspiring AI scientists.
面向高中至大学阶段学习者的综合学习路径，涵盖数学基础、计算机科学核心、密码学原理及网络安全工程实践，旨在为网络安全职业发展奠定坚实基础。
本学习路径旨在引导学习者从计算机网络基础知识出发，逐步深入云计算核心技术，最终具备云计算工程实践能力。路径涵盖网络基础、虚拟化、容器、编排、云原生及自动化运维等关键领域，适合希望从事云计算相关职业的计算机科学与软件工程背景学习者。
本学习路径面向中级学习者，从材料科学基础出发，逐步深入材料结构与性能、表征方法、纳米材料合成与特性，最终导向纳米技术应用与前沿探索，融合化学、物理与工程视角。