graph LR
Core_Course_Notebooks["Core Course Notebooks"]
Supplementary_Learning_Notebooks["Supplementary Learning Notebooks"]
Course_Data_Assets["Course Data Assets"]
Course_Visual_Assets["Course Visual Assets"]
Online_Course_Platform["Online Course Platform"]
Core_Course_Notebooks -- "Uses Data From" --> Course_Data_Assets
Core_Course_Notebooks -- "References Visuals From" --> Course_Visual_Assets
Core_Course_Notebooks -- "Organized By" --> Online_Course_Platform
Core_Course_Notebooks -- "Informs" --> Supplementary_Learning_Notebooks
Supplementary_Learning_Notebooks -- "Uses Data From" --> Course_Data_Assets
Supplementary_Learning_Notebooks -- "References Visuals From" --> Course_Visual_Assets
Supplementary_Learning_Notebooks -- "Organized By" --> Online_Course_Platform
Supplementary_Learning_Notebooks -- "Supplements" --> Core_Course_Notebooks
Course_Data_Assets -- "Provides Data To" --> Core_Course_Notebooks
Course_Data_Assets -- "Provides Data To" --> Supplementary_Learning_Notebooks
Course_Visual_Assets -- "Provides Visuals To" --> Core_Course_Notebooks
Course_Visual_Assets -- "Provides Visuals To" --> Supplementary_Learning_Notebooks
Course_Visual_Assets -- "Integrated By" --> Online_Course_Platform
Online_Course_Platform -- "Structures Content From" --> Core_Course_Notebooks
Online_Course_Platform -- "Structures Content From" --> Supplementary_Learning_Notebooks
Online_Course_Platform -- "Integrates" --> Course_Visual_Assets
click Core_Course_Notebooks href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main//mlcourse.ai/Core_Course_Notebooks.md" "Details"
The mlcourse.ai project is fundamentally structured around delivering machine learning educational content through Jupyter notebooks, supported by various assets, and presented via an online platform. The analysis consolidates the initial CFG and Source Analysis into five core components, highlighting their responsibilities, key source files, and interrelationships.
This component encompasses all primary educational content, including theoretical topics, practical assignments, and individual projects. These notebooks form the main curriculum and are the central interface for user learning and interaction. They are designed to guide users through fundamental and advanced machine learning concepts.
Related Classes/Methods:
jupyter_english/(1:1)jupyter_russian/(1:1)jupyter_chinese/(1:1)jupyter_french/(1:1)
This component specifically covers tutorial notebooks, offering deeper dives into specific tools, libraries, or advanced techniques. They serve to enhance understanding beyond the core curriculum, providing specialized knowledge and practical examples.
Related Classes/Methods:
jupyter_english/tutorials/(1:1)jupyter_russian/tutorials/(1:1)jupyter_chinese/tutorials/(1:1)jupyter_french/tutorials/(1:1)
This component is the central repository for all datasets required for the practical exercises and projects within the notebooks. It provides the raw input for all data analysis, model training, and evaluation tasks, making it a critical resource for hands-on learning.
Related Classes/Methods:
data/(1:1)
This component stores all images, diagrams, and other visual aids used across the notebooks and the online course platform. These assets are crucial for enhancing explanations, illustrating concepts, and visualizing results, thereby improving comprehension.
Related Classes/Methods:
img/(1:1)
This component represents the Jupyter Book configuration, which defines the structure, navigation, and overall presentation of the entire course content online. It acts as the central orchestrator, binding all the educational content and visual resources into a cohesive, accessible, and structured learning experience.
Related Classes/Methods:
mlcourse_ai_jupyter_book/_config.yml(1:1)mlcourse_ai_jupyter_book/_toc.yml(1:1)