机器学习工程学习路线
For 机器学习工程师结构化学习路径, the strongest matches are amai-gmbh/ai-expert-roadmap (This repository provides a structured learning roadmap and competency), mleveryday/100-days-of-ml-code (This repository is a structured 100-day curriculum that covers) and mrdbourke/machine-learning-roadmap (This repository delivers a structured curriculum that spans math). microsoft/ml-for-beginners and harvard-edge/cs249r_book round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
掌握机器学习工程与数据科学技能的综合学习路径与课程资源。
机器学习工程学习路线
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- amai-gmbh/ai-expert-roadmap
AMAI-GmbH/AI-Expert-Roadmap
31,091在 GitHub 上查看This project is a professional development repository that provides structured learning paths for individuals pursuing careers in data-centric engineering and artificial intelligence. It functions as a competency benchmarking framework, defining the core knowledge areas and technical milestones required to achieve proficiency in specialized domains. The repository distinguishes itself through hierarchical knowledge graphing, which organizes complex technical subjects into nested tree structures to create clear, progressive learning sequences. By centralizing curated educational resources and
This repository provides a structured learning roadmap and competency framework for AI and data science, covering machine learning and deep learning topics with curated resources; it fits the search for a machine learning engineer learning path, though it may not explicitly emphasize MLOps or project-based learning.
JavaScriptCurriculum MappingsLearning PathsLearning Roadmaps - mleveryday/100-days-of-ml-code
MLEveryday/100-Days-Of-ML-Code
22,232在 GitHub 上查看100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier
This repository is a structured 100-day curriculum that covers machine learning fundamentals, math foundations, and hands-on algorithm implementations, making it a genuine learning roadmap; it lacks explicit prerequisites and MLOps coverage, which prevents it from being the most comprehensive option.
Jupyter NotebookMathematics for Machine LearningLearning Path GuidesSupervised Learning Models - mrdbourke/machine-learning-roadmap
mrdbourke/machine-learning-roadmap
7,871在 GitHub 上查看This project is a technical curriculum and learning path for machine learning, providing a structured sequence of mathematical foundations, core concepts, and professional workflows. It serves as a comprehensive guide and resource index that connects theoretical principles to the specific software libraries and tools used in real-world implementation. The repository functions as a project workflow blueprint, outlining the sequential steps required to solve machine learning problems from initial discovery through to final deployment. It maps theoretical mathematical principles to practical app
This repository delivers a structured curriculum that spans math foundations, machine learning, and deployment workflows, with project blueprints and resource recommendations — exactly the comprehensive learning path you're looking for.
Curriculum Mappings - microsoft/ml-for-beginners
microsoft/ML-For-Beginners
86,919在 GitHub 上查看This project is an open-source educational curriculum designed to provide a structured path for developers to master machine learning and generative AI. It functions as a technical skill development platform, offering comprehensive study materials that guide learners through fundamental concepts, algorithms, and the practical implementation of artificial intelligence models from scratch. The curriculum distinguishes itself through a pedagogy centered on interactive Jupyter Notebooks, which allow students to execute code cells directly within narrative documents for immediate visual feedback.
This repository is a structured beginner curriculum for machine learning and generative AI with interactive notebooks, which aligns with a learning path but lacks explicit coverage of advanced skills like MLOps, math foundations, and project-based milestones that an ML engineer roadmap typically includes.
Jupyter NotebookLearning Roadmaps - harvard-edge/cs249r_book
harvard-edge/cs249r_book
20,217在 GitHub 上查看This project is a comprehensive educational framework designed to teach the design, deployment, and performance optimization of machine learning systems. It provides a structured curriculum that covers the full stack of artificial intelligence engineering, ranging from the construction of core framework components like tensors and automatic differentiation engines to the orchestration of large-scale distributed training clusters. The platform distinguishes itself through its integration of physics-grounded systems modeling and interactive simulation environments. Users can experiment with dis
This is a structured textbook and curriculum from Harvard covering the entire ML engineering stack—from tensor fundamentals to distributed training and deployment—making it a comprehensive, high-quality learning roadmap for aspiring machine learning engineers.
JavaScriptDeep Learning FrameworksMachine Learning EducationMachine Learning Systems - roboticcam/machine-learning-notes
roboticcam/machine-learning-notes
9,582在 GitHub 上查看This project is a machine learning study guide and technical knowledge base. It serves as a version-controlled repository of mathematical formulas and algorithmic explanations, providing instructional material and reference notes for the study of artificial intelligence. The content is structured as a markdown-based knowledge base that pairs theoretical mathematical explanations directly with code implementations. This approach demonstrates model mechanics in practice across several specialized domains, including deep learning research, probabilistic graphical modeling, and reinforcement lear
This repository is a structured markdown-based study guide and technical knowledge base covering machine learning mathematics, algorithms, and deep learning, with tags indicating learning paths and curricula, making it a relevant resource for a machine learning engineer roadmap.
Jupyter NotebookLearning PathsDeep Learning Architectures - mrdbourke/zero-to-mastery-ml
mrdbourke/zero-to-mastery-ml
5,839在 GitHub 上查看This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr
This repository offers a structured curriculum of interactive Jupyter Notebooks covering Python data science, scikit-learn, and TensorFlow for machine learning and deep learning—a genuine learning path for core ML skills—but it does not explicitly outline prerequisites, math foundations, MLOps, or recommended resources in the roadmap format you seek.
Jupyter NotebookDeep Learning ArchitecturesDeep Learning CoursesUnsupervised Learning - dformoso/machine-learning-mindmap
dformoso/machine-learning-mindmap
6,254在 GitHub 上查看This project is a machine learning knowledge map and educational resource that provides a structured learning path for data science. It organizes core concepts, from basic data analysis to deep learning, into a visual guide and markdown-based knowledge graph. The resource connects theoretical foundations and mathematical concepts to practical execution through links to runnable notebooks and implementation examples. This allows for a transition from conceptual study to hands-on practice. The project uses hierarchical node organization and modular topic decomposition to visualize relationship
dformoso/machine-learning-mindmap is a structured knowledge map and educational resource that organizes machine learning concepts from basic data analysis to deep learning, making it a fitting learning roadmap even though it is more of a visual mindmap than a step-by-step curriculum and lacks explicit MLOps coverage.
Data Science LearningConcept MindmapsConceptual Visualizations - chris-chris/ml-engineer-roadmap
chris-chris/ml-engineer-roadmap
2,204在 GitHub 上查看This project is a structured learning framework designed to guide individuals through the professional requirements of a career in machine learning engineering. It functions as a comprehensive curriculum that organizes complex technical topics and theoretical foundations into a logical, sequential path for skill development. The roadmap visualizes career trajectories, mapping the progression from entry-level positions to advanced technical leadership roles. By breaking down the essential competencies needed for data science and artificial intelligence, it provides a clear overview of the mile
This repository is a roadmap specifically for becoming a machine learning engineer, which directly matches the search for a curated learning path; however, it is a work-in-progress from 2020 with no detailed content visible, so it may not cover all the features you're looking for.
Skill Paths
