courses

Processes structured course metadata from a CSV file and generates formatted markdown tables with visual difficulty indicators, category tags, and hyperlinked course titles. The automation script (generate.py) reads CSV columns (topic, format, difficulty, release_year, price, url, author), transforms difficulty numeric values (1-3) into visual representations (green squares), and inserts the rendered table into README.md at marked insertion points using token-based placeholder detection. This decouples data storage from presentation, enabling contributors to add courses via CSV without markdown formatting knowledge.

Generates translated versions of the main README file in multiple languages (detected from language-specific README files in the repository root), applying language-specific course filtering and localized metadata labels. The system maintains a single CSV source of truth while producing language-specific markdown outputs with translated category names, difficulty labels, and instructional text. Each language version can be independently updated by running the automation script with language-specific configuration, ensuring consistency across translations while allowing community translators to contribute language files.

Stores course URLs in the 'url' field of CSV and generates clickable hyperlinks in markdown tables during table generation, enabling direct access to course resources. The URL field contains the full course link (e.g., 'https://youtube.com/...'), which is rendered as a markdown hyperlink in the generated tables, allowing learners to click directly to the course. This provides seamless navigation from the course collection to actual learning resources.

Defines and enforces a structured schema for course metadata (topic, format, difficulty, release_year, price, url, author, title) stored in CSV format, enabling programmatic filtering, sorting, and validation of course entries. The schema maps each CSV column to a specific data type and semantic meaning (e.g., difficulty as integer 1-3, price as categorical 'free'/'paid', format as enumerated type like 'YouTube playlist'). Validation occurs during CSV parsing, detecting missing fields, invalid difficulty levels, and malformed URLs before table generation, ensuring data quality across contributions.

Provides a contribution framework that guides community members to add new courses by editing a single CSV file rather than markdown, reducing formatting barriers and enabling non-technical contributors to participate. The workflow includes documentation (CONTRIBUTING.md) explaining the CSV schema, example entries, and step-by-step instructions for adding courses, submitting pull requests, and translating content. The structured data approach means contributors only need to fill in CSV columns (title, url, topic, difficulty, etc.) without understanding markdown syntax, lowering the barrier to entry for course curation.

Organizes courses into semantic categories (Deep Learning, Natural Language Processing, Computer Vision, MLOps, Multimodal, etc.) stored as the 'topic' field in CSV, enabling filtering and display of courses by subject area. The system maps topic values to category labels displayed in markdown tables, allowing users to quickly find courses relevant to their learning goals. Topics are rendered as inline category tags in the generated markdown, making it easy to scan courses by subject and enabling programmatic filtering for course recommendation systems.

Assigns difficulty levels (1-3 scale) to courses and encodes them visually in markdown tables using Unicode square symbols (e.g., 🟩🟩 for level 2), enabling learners to quickly assess course complexity without reading descriptions. The difficulty mapping is defined in the automation script (DIFFICULTY_MAP constant) and transforms numeric CSV values into visual representations during table generation. This provides at-a-glance difficulty assessment in the rendered markdown, helping learners self-select courses matching their skill level.

Classifies courses by delivery format (YouTube playlist, university course, blog series, book, interactive tutorial, etc.) stored as the 'format' field in CSV, enabling learners to filter by preferred learning modality. The format field indicates the type of educational resource, helping learners choose courses matching their learning style (video-based, text-based, interactive, etc.). Format values are displayed in markdown tables, providing quick identification of resource type without requiring detailed course descriptions.

Captures and displays the release year of each course (stored as 'release_year' field in CSV) to help learners assess content currency and relevance. The release year is included in course metadata and can be used to filter for recent courses or identify outdated content. This enables learners to prioritize newer courses covering recent AI developments and helps maintainers identify courses that may need updating or replacement.

Tracks course pricing information (free vs. paid) in the 'price' field of CSV, enabling learners to filter for free educational resources and maintainers to prioritize freely-available courses. The price field is categorical ('free' or 'paid') and displayed in markdown tables, helping learners identify no-cost learning opportunities. This supports the repository's mission to provide accessible AI education by highlighting free resources.

Captures course author or institution (stored as 'author' field in CSV) to provide source attribution and enable learners to identify courses from trusted educators or organizations. The author field displays the creator or institution offering the course (e.g., 'Stanford University', 'Jeremy Howard'), helping learners assess course credibility and find courses from preferred educators. This supports academic integrity and helps learners identify high-quality resources from recognized experts.