Machine Learning Course Knowledge Bundle
Bundle OKF 0.1 · 5 conceitos · DerAndr/machine_learning_course_basics
Open source Repository Open in the app JSON README (API)
About
# Machine Learning Course Knowledge Bundle
This bundle is the public-safe content backbone for an interactive machine learning textbook. It keeps concise concepts, learning paths, and lab descriptions separate from renderer code and generated site files.
## Interactive textbook
Use the textbook for connected concept explanations, guided learning paths, and browser labs. Start with the course overview or follow a topic route at your own pace.
## Fast interactive reviews
Fast reviews use a short explanation → exploration → quiz → feedback loop for focused practice. They complement the connected textbook and full lecture materials rather than replacing either one. The textbook renderer discovers verified, self-contained review artifacts and makes them available from the homepage and every page sidebar.
* [Supervised learning](supervised-learning/) - Concise concepts and learning paths for supervised machine learning.
* [Learning paths](learning-paths/) - Guided routes through select
Details
- Kind
- OKF bundles
- Topic
- No topic detected
- Publisher
- derandr
- Origin
- okf_github
- Category
- dados
- Version
- 0.1
- Stars
- 16
- Forks
- 3
- Last push
- 2026-07-21T01:56:57Z
- Repository state
- ativo
- Language
- Jupyter Notebook
- License
- MIT
- Added
- 2026-09-08 22:07:22
- Updated
- 2026-09-08 22:07:22
- Origin id
DerAndr/machine_learning_course_basics:okf/index.md
README
# Machine Learning Course [](LICENSE) [](LICENSE-CONTENT) An introductory machine learning course with connected textbook pages, focused interactive reviews, lecture notes and slides, and hands-on notebooks. ## Build Week project: Learning Companions Learning Companions is a reusable Codex skill that turns repositories and knowledge bases into short interactive lessons. The ML course is its first repository-specific implementation. [Open the portable skill](.agents/skills/interactive-learning-experience-builder/) · [Try a generated lesson](https://derandr.github.io/machine_learning_course_basics/demos/lecture_05_classification_part_1/) · [How Codex and GPT-5.6 were used](#how-codex-and-gpt-56-were-used) The course materials existed before Build Week. During the event, I built the portable skill, repository adapter, generator, validator, interaction runtime, and generated learning companions. ## Choose how to learn | Route | Best for | Open | |---|---|---| | Interactive textbook | Connected explanations, learning paths, and browser labs | [Open the textbook](https://derandr.github.io/machine_learning_course_basics/) | | Fast interactive reviews | Short explanation → exploration → quiz → feedback practice | [Choose a review](#fast-interactive-reviews) | | Full lecture materials | Notes, slides, examples, and topic references | [Browse all lectures](lectures/README.md) | | Hands-on notebooks | Guided examples and 90-minute practical sessions | [Choose from the course map](#course-map) | ## Fast interactive reviews These self-contained learning companions are quick, accessible reviews that work online or directly from a cloned repository. They complement the textbook and full lectures. | Topic | Live | Offline | |---|---|---| | 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) | | 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) | | 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) | The reviews share one portable learning loop but use interactions chosen for the topic: - **EDA:** binning, IQR fences, association, and missingness. - **Regression:** residual diagnostics, Ridge and Lasso shrinkage, and MAE and RMSE sensitivity. - **Classification:** threshold and confusion matrix outcomes plus a class-aware decision boundary. Each review also includes three quiz levels, immediate feedback, keyboard support, reduced-motion behavior, accessible fallbacks, and focus-friendly and color-blind-safe controls. ## Course map | # | Lecture | Notes | Practical | |---|---|---|---| | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 19 | [Course Overview](lectures/lecture_19_course_overview/README.md) | [notes](lectures/lecture_19_course_overview/lecture_notes.md) | — | Lectures 01–14 are fully packaged with slide decks and example notebooks. Lectures 15–19 are practical-first drafts and the final course overview. The repository also includes a [mini-project on NYC Airbnb price prediction](mini_projects/airbnb_nyc/README.md) and [example midterm questions](docs/midterm_examples.md). ## Create interactive learning materials Use the skill that matches the source context: | Goal | Skills | |---|---| | Create an experience from a general repository or knowledge base | `$interactive-learning-experience-builder` | | Create a review for this ML course | `$ml-course-interactive-learning-assistant` with `$interactive-learning-experience-builder` | The general skill owns the reusable content contract, deterministic generator, and offline validator. The course skill adds this repository's source, public-safety, output, and publishing rules. Follow the [student prompt and installation quickstart](docs/student-learning-companion-quickstart.md) to use the skills locally or add the generic skill to your personal Codex. Use the [operational learning-companion guide](docs/interactive-lecture-learning-assistant.md) for the full authoring flow, or read the [Learning companions architecture](docs/learning-companions-architecture.md) for responsibility boundaries. ## How Codex and GPT-5.6 were used This course repository existed before Build Week. During the event it was meaningfully extended from a collection of learning resources into a reusable learning-companion workflow and a three-topic interactive showcase. - **Codex** was used to navigate the repository, turn the teaching problem into a scoped architecture, implement the portable skill and ML-course adapter, integrate the generated reviews, and run automated and browser-based verification. - **GPT-5.6** supported architecture synthesis, source-grounded transformation of lecture materials, quiz and feedback design, and review of the implementation and documentation. - **Human decisions remained central:** the teacher defined the learner and attention problem, selected authoritative sources, approved the core-versus-adapter boundary, chose accessibility and public-safety rules, and reviewed the resulting learning experience. Judges and contributors can follow the [operational guide](docs/interactive-lecture-learning-assistant.md), inspect the [integration evidence](docs/build-week-integration-evidence.md), and use the [submission and testing worksheet](docs/build-week-submission-preparation.md) for a concise end-to-end test path. ## Local setup Use `uv` as the environment manager: ```bash uv sync uv run python tools/check_notebook_environment.py uv run jupyter lab ``` See the [student quickstart](docs/student-quickstart.md) for platform details and optional dependency groups. ## Contributing and reference - [Student and maintainer textbook workflow](docs/contributing-to-textbook.md) - [Student learning-companion prompts and setup](docs/student-learning-companion-quickstart.md) - [OKF authoring guide](docs/okf-authoring-guide.md) - [Interactive learning-companion operations](docs/interactive-lecture-learning-assistant.md) - [Learning companions architecture](docs/learning-companions-architecture.md) - [Deep-learning and Colab guide](docs/deep-learning-colab-guide.md) - [Agent repository guide](AGENTS.md) ## Repository map - `lectures/` — lecture notes, slides, examples, practicals, and metadata - `lecture_experiences/` — grounded payloads and canonical standalone reviews - `okf/` — durable textbook concepts, learning paths, labs, and metadata - `site/assets/` and `site/data/` — browser assets and public-safe lab data - `tools/` — validation, rendering, environment, and maintenance commands - `tests/` — course, textbook, skill, and learning-companion regression checks - `docs/` — setup, contribution, architecture, and authoring guidance - `src/mlcourse/` — shared Python helpers Generated textbook output lives under `site/_build/` and is not committed. ## License Unless stated otherwise: - Source code in `src/`, `tests/`, `tools/`, and standalone `.py` files is licensed under the [MIT License](LICENSE). - Lecture notes, slide decks, PDFs, images, notebooks, and other course content are licensed under [CC BY-NC-SA 4.0](LICENSE-CONTENT).