{
  "markdown": "# Machine Learning Course\n\n[![License: MIT](https://img.shields.io/badge/Code-MIT-blue.svg)](LICENSE)\n[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/Content-CC%20BY--NC--SA%204.0-lightgrey.svg)](LICENSE-CONTENT)\n\nAn introductory machine learning course with connected textbook pages, focused\ninteractive reviews, lecture notes and slides, and hands-on notebooks.\n\n## Build Week project: Learning Companions\n\nLearning Companions is a reusable Codex skill that turns repositories and\nknowledge bases into short interactive lessons. The ML course is its first\nrepository-specific implementation.\n\n[Open the portable skill](.agents/skills/interactive-learning-experience-builder/)\n· [Try a generated lesson](https://derandr.github.io/machine_learning_course_basics/demos/lecture_05_classification_part_1/)\n· [How Codex and GPT-5.6 were used](#how-codex-and-gpt-56-were-used)\n\nThe course materials existed before Build Week. During the event, I built the\nportable skill, repository adapter, generator, validator, interaction runtime,\nand generated learning companions.\n\n## Choose how to learn\n\n| Route | Best for | Open |\n|---|---|---|\n| Interactive textbook | Connected explanations, learning paths, and browser labs | [Open the textbook](https://derandr.github.io/machine_learning_course_basics/) |\n| Fast interactive reviews | Short explanation → exploration → quiz → feedback practice | [Choose a review](#fast-interactive-reviews) |\n| Full lecture materials | Notes, slides, examples, and topic references | [Browse all lectures](lectures/README.md) |\n| Hands-on notebooks | Guided examples and 90-minute practical sessions | [Choose from the course map](#course-map) |\n\n## Fast interactive reviews\n\nThese self-contained learning companions are quick, accessible reviews that\nwork online or directly from a cloned repository. They complement the textbook\nand full lectures.\n\n| Topic | Live | Offline |\n|---|---|---|\n| Exploratory Data Analysis | [Open live review](https://derandr.github.io/machine_learning_course_basics/demos/lecture_01_eda/) | [`lecture_experiences/lecture_01_eda/index.html`](lecture_experiences/lecture_01_eda/index.html) |\n| Regression | [Open live review](https://derandr.github.io/machine_learning_course_basics/demos/lecture_04_regression/) | [`lecture_experiences/lecture_04_regression/index.html`](lecture_experiences/lecture_04_regression/index.html) |\n| Classification Part 1 | [Open live review](https://derandr.github.io/machine_learning_course_basics/demos/lecture_05_classification_part_1/) | [`lecture_experiences/lecture_05_classification_part_1/index.html`](lecture_experiences/lecture_05_classification_part_1/index.html) |\n\nThe reviews share one portable learning loop but use interactions chosen for\nthe topic:\n\n- **EDA:** binning, IQR fences, association, and missingness.\n- **Regression:** residual diagnostics, Ridge and Lasso shrinkage, and\n  MAE and RMSE sensitivity.\n- **Classification:** threshold and confusion matrix outcomes plus a\n  class-aware decision boundary.\n\nEach review also includes three quiz levels, immediate feedback, keyboard\nsupport, reduced-motion behavior, accessible fallbacks, and focus-friendly and\ncolor-blind-safe controls.\n\n## Course map\n\n| # | Lecture | Notes | Practical |\n|---|---|---|---|\n| 01 | [Exploratory Data Analysis](lectures/lecture_01_eda/README.md) | [notes](lectures/lecture_01_eda/lecture_notes.md) | [practical](lectures/lecture_01_eda/practical_session/README.md) |\n| 02 | [Data Preparation Part 1](lectures/lecture_02_data_preparation_part_1/README.md) | [notes](lectures/lecture_02_data_preparation_part_1/lecture_notes.md) | [practical](lectures/lecture_02_data_preparation_part_1/practical_session/README.md) |\n| 03 | [Data Preparation Part 2](lectures/lecture_03_data_preparation_part_2/README.md) | [notes](lectures/lecture_03_data_preparation_part_2/lecture_notes.md) | [practical](lectures/lecture_03_data_preparation_part_2/practical_session/README.md) |\n| 04 | [Regression](lectures/lecture_04_regression/README.md) | [notes](lectures/lecture_04_regression/lecture_notes.md) | [practical](lectures/lecture_04_regression/practical_session/README.md) |\n| 05 | [Classification Part 1](lectures/lecture_05_classification_part_1/README.md) | [notes](lectures/lecture_05_classification_part_1/lecture_notes.md) | [practical](lectures/lecture_05_classification_part_1/practical_session/README.md) |\n| 06 | [Classification Part 2](lectures/lecture_06_classification_part_2/README.md) | [notes](lectures/lecture_06_classification_part_2/lecture_notes.md) | [practical](lectures/lecture_06_classification_part_2/practical_session/README.md) |\n| 07 | [Ensembles](lectures/lecture_07_ensembles/README.md) | [notes](lectures/lecture_07_ensembles/lecture_notes.md) | [practical](lectures/lecture_07_ensembles/practical_session/README.md) |\n| 08 | [Time Series](lectures/lecture_08_time_series/README.md) | [notes](lectures/lecture_08_time_series/lecture_notes.md) | [practical](lectures/lecture_08_time_series/practical_session/README.md) |\n| 09 | [Clustering](lectures/lecture_09_clustering/README.md) | [notes](lectures/lecture_09_clustering/lecture_notes.md) | [practical](lectures/lecture_09_clustering/practical_session/README.md) |\n| 10 | [Cross-Validation and HPO](lectures/lecture_10_cross_validation_hpo/README.md) | [notes](lectures/lecture_10_cross_validation_hpo/lecture_notes.md) | [practical](lectures/lecture_10_cross_validation_hpo/practical_session/README.md) |\n| 11 | [Explainability and Interpretability](lectures/lecture_11_explainability_interpretability/README.md) | [notes](lectures/lecture_11_explainability_interpretability/lecture_notes.md) | [practical](lectures/lecture_11_explainability_interpretability/practical_session/README.md) |\n| 12 | [Introduction to Neural Networks](lectures/lecture_12_intro_neural_networks/README.md) | [notes](lectures/lecture_12_intro_neural_networks/lecture_notes.md) | [practical](lectures/lecture_12_intro_neural_networks/practical_session/README.md) |\n| 13 | [Responsible AI](lectures/lecture_13_responsible_ai/README.md) | [notes](lectures/lecture_13_responsible_ai/lecture_notes.md) | [practical](lectures/lecture_13_responsible_ai/practical_session/README.md) |\n| 14 | [ML in Production](lectures/lecture_14_ml_in_production/README.md) | [notes](lectures/lecture_14_ml_in_production/lecture_notes.md) | [practical](lectures/lecture_14_ml_in_production/practical_session/README.md) |\n| 15 | [Computer Vision](lectures/lecture_15_computer_vision/README.md) | [notes](lectures/lecture_15_computer_vision/lecture_notes.md) | [practical](lectures/lecture_15_computer_vision/practical_session/README.md) |\n| 16 | [Natural Language Processing](lectures/lecture_16_nlp_overview/README.md) | [notes](lectures/lecture_16_nlp_overview/lecture_notes.md) | [practical](lectures/lecture_16_nlp_overview/practical_session/README.md) |\n| 17 | [Recommender Systems](lectures/lecture_17_recsys/README.md) | [notes](lectures/lecture_17_recsys/lecture_notes.md) | [practical](lectures/lecture_17_recsys/practical_session/README.md) |\n| 18 | [LLM Overview](lectures/lecture_18_llm_overview/README.md) | [notes](lectures/lecture_18_llm_overview/lecture_notes.md) | [practical](lectures/lecture_18_llm_overview/practical_session/README.md) |\n| 19 | [Course Overview](lectures/lecture_19_course_overview/README.md) | [notes](lectures/lecture_19_course_overview/lecture_notes.md) | — |\n\nLectures 01–14 are fully packaged with slide decks and example notebooks.\nLectures 15–19 are practical-first drafts and the final course overview. The\nrepository also includes a [mini-project on NYC Airbnb price prediction](mini_projects/airbnb_nyc/README.md)\nand [example midterm questions](docs/midterm_examples.md).\n\n## Create interactive learning materials\n\nUse the skill that matches the source context:\n\n| Goal | Skills |\n|---|---|\n| Create an experience from a general repository or knowledge base | `$interactive-learning-experience-builder` |\n| Create a review for this ML course | `$ml-course-interactive-learning-assistant` with `$interactive-learning-experience-builder` |\n\nThe general skill owns the reusable content contract, deterministic generator,\nand offline validator. The course skill adds this repository's source,\npublic-safety, output, and publishing rules. Follow the\n[student prompt and installation quickstart](docs/student-learning-companion-quickstart.md)\nto use the skills locally or add the generic skill to your personal Codex. Use\nthe\n[operational learning-companion guide](docs/interactive-lecture-learning-assistant.md)\nfor the full authoring flow, or read the\n[Learning companions architecture](docs/learning-companions-architecture.md)\nfor responsibility boundaries.\n\n## How Codex and GPT-5.6 were used\n\nThis course repository existed before Build Week. During the event it was\nmeaningfully extended from a collection of learning resources into a reusable\nlearning-companion workflow and a three-topic interactive showcase.\n\n- **Codex** was used to navigate the repository, turn the teaching problem into\n  a scoped architecture, implement the portable skill and ML-course adapter,\n  integrate the generated reviews, and run automated and browser-based\n  verification.\n- **GPT-5.6** supported architecture synthesis, source-grounded transformation\n  of lecture materials, quiz and feedback design, and review of the\n  implementation and documentation.\n- **Human decisions remained central:** the teacher defined the learner and\n  attention problem, selected authoritative sources, approved the\n  core-versus-adapter boundary, chose accessibility and public-safety rules,\n  and reviewed the resulting learning experience.\n\nJudges and contributors can follow the\n[operational guide](docs/interactive-lecture-learning-assistant.md), inspect\nthe [integration evidence](docs/build-week-integration-evidence.md), and use\nthe [submission and testing worksheet](docs/build-week-submission-preparation.md)\nfor a concise end-to-end test path.\n\n## Local setup\n\nUse `uv` as the environment manager:\n\n```bash\nuv sync\nuv run python tools/check_notebook_environment.py\nuv run jupyter lab\n```\n\nSee the [student quickstart](docs/student-quickstart.md) for platform details\nand optional dependency groups.\n\n## Contributing and reference\n\n- [Student and maintainer textbook workflow](docs/contributing-to-textbook.md)\n- [Student learning-companion prompts and setup](docs/student-learning-companion-quickstart.md)\n- [OKF authoring guide](docs/okf-authoring-guide.md)\n- [Interactive learning-companion operations](docs/interactive-lecture-learning-assistant.md)\n- [Learning companions architecture](docs/learning-companions-architecture.md)\n- [Deep-learning and Colab guide](docs/deep-learning-colab-guide.md)\n- [Agent repository guide](AGENTS.md)\n\n## Repository map\n\n- `lectures/` — lecture notes, slides, examples, practicals, and metadata\n- `lecture_experiences/` — grounded payloads and canonical standalone reviews\n- `okf/` — durable textbook concepts, learning paths, labs, and metadata\n- `site/assets/` and `site/data/` — browser assets and public-safe lab data\n- `tools/` — validation, rendering, environment, and maintenance commands\n- `tests/` — course, textbook, skill, and learning-companion regression checks\n- `docs/` — setup, contribution, architecture, and authoring guidance\n- `src/mlcourse/` — shared Python helpers\n\nGenerated textbook output lives under `site/_build/` and is not committed.\n\n## License\n\nUnless stated otherwise:\n\n- Source code in `src/`, `tests/`, `tools/`, and standalone `.py` files is\n  licensed under the [MIT License](LICENSE).\n- Lecture notes, slide decks, PDFs, images, notebooks, and other course content\n  are licensed under [CC BY-NC-SA 4.0](LICENSE-CONTENT).\n",
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