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Covers linear regression, logistic regression, ne...\n\nTags: latest:1.0.1\n\nVersion history:\n\nv1.0.1 | 2026-04-20T14:19:37.696Z | user\n\nFix: tone down bytesagain attribution, keep it natural\n\nv1.0.0 | 2026-04-20T14:15:36.388Z | user\n\nLaunch: ML algorithm explainer with math, Python code, Jupyter notebooks, and learning paths\n\nArchive index:\n\nArchive v1.0.1: 4 files, 7734 bytes\n\nFiles: scripts/ml-notebook-finder.sh (17821b), skill-card.md (2490b), SKILL.md (2506b), _meta.json (150b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: homemade-machine-learning-skill\ndescription: \"Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos.\"\nversion: \"1.0.0\"\nauthor: \"BytesAgain\"\nhomepage: \"https://bytesagain.com/skill/homemade-machine-learning-skill\"\ntags: [\"machine learning\", \"jupyter notebook\", \"python\", \"data science\", \"education\", \"linear regression\", \"neural network\", \"skill\"]\n---\n\n# Homemade Machine Learning Skill\n\nMachine learning skill: learn, explain, and implement ML algorithms from scratch.\nBased on [trekhleb/homemade-machine-learning](https://github.com/trekhleb/homemade-machine-learning) (MIT, 22k+ ⭐)\n\n> 📦 Install: `clawhub install homemade-machine-learning-skill`\n\n5 algorithms · 11 interactive notebooks · math explained · Python code included\n\n## Commands\n\n### explain — 解释算法原理 + 数学 + 代码\n```bash\nbash scripts/ml-notebook-finder.sh explain \"linear regression\"\nbash scripts/ml-notebook-finder.sh explain \"neural network\"\nbash scripts/ml-notebook-finder.sh explain \"kmeans\"\n```\n\n### notebook — 获取交互式 Jupyter Notebook 链接\n```bash\nbash scripts/ml-notebook-finder.sh notebook \"logistic regression\"\nbash scripts/ml-notebook-finder.sh notebook \"anomaly detection\"\n```\n\n### code — 获取 Python 实现代码片段\n```bash\nbash scripts/ml-notebook-finder.sh code \"linear regression\"\nbash scripts/ml-notebook-finder.sh code \"kmeans\"\n```\n\n### path — 生成学习路径（按难度排序）\n```bash\nbash scripts/ml-notebook-finder.sh path beginner\nbash scripts/ml-notebook-finder.sh path intermediate\nbash scripts/ml-notebook-finder.sh path advanced\n```\n\n### list — 列出所有算法\n```bash\nbash scripts/ml-notebook-finder.sh list\n```\n\n## Algorithms\n\n| Algorithm | Type | Notebooks | Use Case |\n|-----------|------|-----------|----------|\n| linear regression | supervised | 3 | price prediction, forecasting |\n| logistic regression | supervised | 4 | classification, MNIST |\n| neural network (MLP) | supervised | 2 | image recognition, deep learning |\n| k-means | unsupervised | 1 | clustering, segmentation |\n| anomaly detection | unsupervised | 1 | fraud detection, monitoring |\n\n## Source\nMIT License — Original author: [trekhleb](https://github.com/trekhleb/homemade-machine-learning)\nIndexed by [BytesAgain](https://bytesagain.com) — AI skill discovery platform\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn76vqrf94wk924mddj8fp4p5x8497tb\",\n  \"slug\": \"homemade-machine-learning-skill\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1776694777696\n}\n\nFile v1.0.1:skill-card.md\n\n## Description:\n\nMachine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection, with math, Python code, and skill demos.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[bytesagain-lab](https://clawhub.ai/user/bytesagain-lab)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, data science learners, and external users use this skill to get concise explanations, learning paths, notebook links, and Python examples for foundational machine learning algorithms.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Users may open third-party hosted notebook or source links from the helper output.\n\nMitigation: Review external notebook and source links before opening them, especially in environments with restrictions on third-party hosted content.\n\nRisk: Educational explanations and snippets may be unsuitable as production machine learning implementations without validation.\n\nMitigation: Treat the generated examples as learning material and validate any adapted code against the intended dataset, model requirements, and review process.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/bytesagain-lab/skills/homemade-machine-learning-skill)\n- [Publisher profile](https://clawhub.ai/user/bytesagain-lab)\n- [BytesAgain skill homepage](https://bytesagain.com/skill/homemade-machine-learning-skill)\n- [homemade-machine-learning source project](https://github.com/trekhleb/homemade-machine-learning)\n- [homemade-machine-learning notebooks](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown-style terminal text with inline shell commands, Python code snippets, and external notebook links]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The packaged shell helper prints static educational content and links; it does not fetch or execute remote code.]\n\n## Skill Version(s):\n\n1.0.1 (source: ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.0: 3 files, 6508 bytes\n\nFiles: scripts/ml-notebook-finder.sh (17821b), SKILL.md (2622b), _meta.json (150b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: homemade-machine-learning-skill\ndescription: \"Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos.\"\nversion: \"1.0.0\"\nauthor: \"BytesAgain\"\nhomepage: \"https://bytesagain.com/skill/homemade-machine-learning-skill\"\ntags: [\"machine learning\", \"jupyter notebook\", \"python\", \"data science\", \"education\", \"linear regression\", \"neural network\", \"skill\"]\n---\n\n# Homemade Machine Learning Skill\n\nMachine learning skill: learn, explain, and implement ML algorithms from scratch.\nBased on [trekhleb/homemade-machine-learning](https://github.com/trekhleb/homemade-machine-learning) (MIT, 22k+ ⭐)\n\n> 🌐 More AI skills at [bytesagain.com](https://bytesagain.com) — 60,000+ indexed skills\n> 📦 Install: `clawhub install homemade-machine-learning-skill`\n\n5 algorithms · 11 interactive notebooks · math explained · Python code included\n\n## Commands\n\n### explain — 解释算法原理 + 数学 + 代码\n```bash\nbash scripts/ml-notebook-finder.sh explain \"linear regression\"\nbash scripts/ml-notebook-finder.sh explain \"neural network\"\nbash scripts/ml-notebook-finder.sh explain \"kmeans\"\n```\n\n### notebook — 获取交互式 Jupyter Notebook 链接\n```bash\nbash scripts/ml-notebook-finder.sh notebook \"logistic regression\"\nbash scripts/ml-notebook-finder.sh notebook \"anomaly detection\"\n```\n\n### code — 获取 Python 实现代码片段\n```bash\nbash scripts/ml-notebook-finder.sh code \"linear regression\"\nbash scripts/ml-notebook-finder.sh code \"kmeans\"\n```\n\n### path — 生成学习路径（按难度排序）\n```bash\nbash scripts/ml-notebook-finder.sh path beginner\nbash scripts/ml-notebook-finder.sh path intermediate\nbash scripts/ml-notebook-finder.sh path advanced\n```\n\n### list — 列出所有算法\n```bash\nbash scripts/ml-notebook-finder.sh list\n```\n\n## Algorithms\n\n| Algorithm | Type | Notebooks | Use Case |\n|-----------|------|-----------|----------|\n| linear regression | supervised | 3 | price prediction, forecasting |\n| logistic regression | supervised | 4 | classification, MNIST |\n| neural network (MLP) | supervised | 2 | image recognition, deep learning |\n| k-means | unsupervised | 1 | clustering, segmentation |\n| anomaly detection | unsupervised | 1 | fraud detection, monitoring |\n\n## Source\nMIT License — Original: github.com/trekhleb/homemade-machine-learning\nSkill by BytesAgain — https://bytesagain.com/skill/homemade-machine-learning-skill\nDiscover more skills: https://bytesagain.com\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn76vqrf94wk924mddj8fp4p5x8497tb\",\n  \"slug\": \"homemade-machine-learning-skill\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776694536388\n}","readmeExcerpt":"Skill: Homemade Machine Learning Skill Owner: bytesagain-lab Summary: Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... Tags: latest:1.0.1 Version history: v1.0.1 | 2026-04-20T14:19:37.696Z | user Fix: tone down bytesagain attribution, keep it natural v1.0.0 | 2026-04-20T14:15:36.388Z | user Launch: ML algor","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh explain \"linear regression\"\nbash scripts/ml-notebook-finder.sh explain \"neural network\"\nbash scripts/ml-notebook-finder.sh explain \"kmeans\""},{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh notebook \"logistic regression\"\nbash scripts/ml-notebook-finder.sh notebook \"anomaly detection\""},{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh code \"linear regression\"\nbash scripts/ml-notebook-finder.sh code \"kmeans\""},{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh path beginner\nbash scripts/ml-notebook-finder.sh path intermediate\nbash scripts/ml-notebook-finder.sh path advanced"},{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh list"},{"language":"bash","snippet":"bash scripts/ml-notebook-finder.sh explain \"linear regression\"\nbash scripts/ml-notebook-finder.sh explain \"neural network\"\nbash scripts/ml-notebook-finder.sh explain \"kmeans\""}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: homemade-machine-learning-skill\ndescription: \"Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos.\"\nversion: \"1.0.0\"\nauthor: \"BytesAgain\"\nhomepage: \"https://bytesagain.com/skill/homemade-machine-learning-skill\"\ntags: [\"machine learning\", \"jupyter notebook\", \"python\", \"data science\", \"education\", \"linear regression\", \"neural network\", \"skill\"]\n---\n\n# Homemade Machine Learning Skill\n\nMachine learning skill: learn, explain, and implement ML algorithms from scratch.\nBased on [trekhleb/homemade-machine-learning](https://github.com/trekhleb/homemade-machine-learning) (MIT, 22k+ ⭐)\n\n> 📦 Install: `clawhub install homemade-machine-learning-skill`\n\n5 algorithms · 11 interactive notebooks · math explained · Python code included\n\n## Commands\n\n### explain — 解释算法原理 + 数学 + 代码\n```bash\nbash scripts/ml-notebook-finder.sh explain \"linear regression\"\nbash scripts/ml-notebook-finder.sh explain \"neural network\"\nbash scripts/ml-notebook-finder.sh explain \"kmeans\"\n```\n\n### notebook — 获取交互式 Jupyter Notebook 链接\n```bash\nbash scripts/ml-notebook-finder.sh notebook \"logistic regression\"\nbash scripts/ml-notebook-finder.sh notebook \"anomaly detection\"\n```\n\n### code — 获取 Python 实现代码片段\n```bash\nbash scripts/ml-notebook-finder.sh code \"linear regression\"\nbash scripts/ml-notebook-finder.sh code \"kmeans\"\n```\n\n### path — 生成学习路径（按难度排序）\n```bash\nbash scripts/ml-notebook-finder.sh path beginner\nbash scripts/ml-notebook-finder.sh path intermediate\nbash scripts/ml-notebook-finder.sh path advanced\n```\n\n### list — 列出所有算法\n```bash\nbash scripts/ml-notebook-finder.sh list\n```\n\n## Algorithms\n\n| Algorithm | Type | Notebooks | Use Case |\n|-----------|------|-----------|----------|\n| linear regression | supervised | 3 | price prediction, forecasting |\n| logistic regression | supervised | 4 | classification, MNIST |\n| neural network (MLP) | supervised | 2 | image recognition, deep learning |\n| k-means | unsupervised | 1 | clustering, segmentation |\n| anomaly detection | unsupervised | 1 | fraud detection, monitoring |\n\n## Source\nMIT License — Original author: [trekhleb](https://github.com/trekhleb/homemade-machine-learning)\nIndexed by [BytesAgain](https://bytesagain.com) — AI skill discovery platform"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn76vqrf94wk924mddj8fp4p5x8497tb\",\n  \"slug\": \"homemade-machine-learning-skill\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1776694777696\n}"},{"path":"skill-card.md","content":"## Description:\n\nMachine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection, with math, Python code, and skill demos.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[bytesagain-lab](https://clawhub.ai/user/bytesagain-lab)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, data science learners, and external users use this skill to get concise explanations, learning paths, notebook links, and Python examples for foundational machine learning algorithms.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Users may open third-party hosted notebook or source links from the helper output.\n\nMitigation: Review external notebook and source links before opening them, especially in environments with restrictions on third-party hosted content.\n\nRisk: Educational explanations and snippets may be unsuitable as production machine learning implementations without validation.\n\nMitigation: Treat the generated examples as learning material and validate any adapted code against the intended dataset, model requirements, and review process.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/bytesagain-lab/skills/homemade-machine-learning-skill)\n- [Publisher profile](https://clawhub.ai/user/bytesagain-lab)\n- [BytesAgain skill homepage](https://bytesagain.com/skill/homemade-machine-learning-skill)\n- [homemade-machine-learning source project](https://github.com/trekhleb/homemade-machine-learning)\n- [homemade-machine-learning notebooks](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown-style terminal text with inline shell commands, Python code snippets, and external notebook links]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The packaged shell helper prints static educational content and links; it does not fetch or execute remote code.]\n\n## Skill Version(s):\n\n1.0.1 (source: ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... Skill: Homemade Machine Learning Skill Owner: bytesagain-lab Summary: Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, ne... 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