mrdbourke/machine-learning-roadmap

Features

• Learning Paths - Provides a structured sequence of mathematical foundations, core concepts, and professional workflows for mastering machine learning.
• Modular Learning Paths - Segments the machine learning domain into a structured sequence of discrete milestones and modular learning paths.
• Data Science Collections - Provides a vetted collection of instructional materials and external guides for studying data science.
• Machine Learning Education - Provides a structured sequence of mathematical foundations and core concepts required to build machine learning models.
• Machine Learning Foundations - Connects foundational mathematics and essential tools into a structured path for mastering machine learning.
• Applied AI Curricula - Maps theoretical mathematical principles to their practical applications in artificial intelligence and data science.
• Hierarchical Knowledge Structures - Organizes technical knowledge into a nested, hierarchical structure to guide learners through the complexity of the subject.
• Curated Learning Resources - Collects high-quality documentation and external instructional guides for self-directed study of complex technical topics.
• Curated Resource Indexes - Provides an organized collection of links and references to external learning materials for deep study of technical topics.
• Curriculum Guides - Serves as a comprehensive guide connecting theoretical principles to software libraries used in real-world implementation.
• Curriculum Mappings - Maps theoretical principles to specific software libraries and tools through a structured sequence of technical topics.
• AI Project Blueprints - Offers a step-by-step technical guide for solving machine learning problems from initial discovery through to final deployment.
• Machine Learning Mathematics - Explains the fundamental mathematical principles that drive the behavior of complex machine learning algorithms.
• Project Workflow Outlines - Outlines the sequential steps required to solve a machine learning problem from discovery to final implementation.
• Technical Learning Paths - Organizes a structured sequence of foundations, problem types, and workflow steps to guide the mastery of the domain.
• Workflow Process Modeling - Details the sequential operational steps of the machine learning pipeline from initial discovery through to final deployment.
• Machine Learning Tooling - Provides resources for discovering and selecting the specific software libraries needed for different development stages.
• Technical Skill Acquisition - Identifies the libraries and tools needed to progress from a beginner to a professional practitioner.
• Problem Type Identification - Defines the characteristics of specific machine learning problems to help users recognize when to apply certain techniques.
• Tool-to-Task Mapping - Maps specific software libraries and frameworks to the exact stages of the machine learning development lifecycle.
• Problem Classification Guides - Provides guides to help users categorize technical tasks and select the appropriate machine learning algorithmic approach.
• ML Problem Framing - Organizes the sequential workflow steps needed to move a machine learning project from discovery to implementation.
• Learning and Reference - Roadmap for learning machine learning concepts and tools.

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