Hii ni Kujifunza Mashine kwa Anayeanza, mtaala wa wiki 12 wenye masomo 26 unaofundisha dhana za kimsingi za kujifunza mashine kwa kutumia Python (hasa na Scikit-learn) na R. Hifadhi hii imetengenezwa kama rasilimali ya kujifunza kwa kasi yako mwenyewe ikiwa na miradi ya vitendo, maswali ya majaribio, na mazoezi. Kila somo linachunguza dhana za ML kupitia data halisi kutoka tamaduni na maeneo mbalimbali duniani. Vipengele muhimu: - Maudhui ya Elimu: Masomo 26 yanayojumuisha utangulizi wa ML, urejeleaji, uainishaji, ugawanyaji, NLP,…
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இது துவக்கத்திற்கான இயந்திரக் கற்றல், Python (முக்கியமாக Scikit-learn) மற்றும் R பயன்படுத்தி இயந்திரக் கற்றல் கருத்துக்களை உள்ளடக்கிய 12 வாரங்கள், 26 பாடங்கள் கொண்ட விரிவான பாடத்திட்டம். இந்த களஞ்சியம் சுயமாக கற்றல் செய்யும் வளமாக வடிவமைக்கப்பட்டுள்ளது, இதில் செயல்முறை திட்டங்கள், வினாடி வினா மற்றும் பணிகள் உள்ளன. ஒவ்வொரு பாடமும் உலகம் முழுவதிலுமுள்ள பல்வேறு கலாச்சாரங்கள் மற்றும் பிராந்தியங்களின் உண்மையான தரவுகளைப் பயன்படுத்தி ML கருத்துக்களை ஆராய்கிறது. முக்கிய கூறுகள்: - கல்வி உள்ளடக்கம்: ML அறிமுகம், பின்வாங்கல், வகைப்படுத்தல், குழுமம், NLP, நேர வரிசை மற்றும் வலியுறுத்தல் கற்றல் ஆகியவற்றை உள்ளடக்கிய 26 பாடங்கள் - வினாடி வினா பயன்பாடு: Vue.js அடிப்படையிலான வினாடி வினா பயன்பாடு, பாடத்திற்கு…
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ఇది Machine Learning for Beginners, పాఠ్యాంశాల 26తో కూడిన 12 వారాల సమగ్ర పాఠ్యక్రమం, ఇది Python (ప్రధానంగా Scikit-learn తో) మరియు R ఉపయోగించి క్లాసిక్ మెషీన్ లెర్నింగ్ కాన్సెప్ట్లను కవర్ చేస్తుంది. ఈ రిపోజిటరీ స్వీయ-గతిలో నేర్చుకునే వనరుగా రూపొందించబడింది, ఇందులో ప్రాక్టికల్ ప్రాజెక్టులు, క్విజ్లు మరియు అసైన్మెంట్లు ఉన్నాయి. ప్రతి పాఠం ప్రపంచవ్యాప్తంగా వివిధ సంస్కృతులు మరియు ప్రాంతాల నుండి వాస్తవ డేటా ద్వారా ML కాన్సెప్ట్లను అన్వేషిస్తుంది. ప్రధాన భాగాలు: - విద్యా విషయాలు: ML పరిచయం, రిగ్రెషన్, క్లాసిఫికేషన్, క్లస్టరింగ్, NLP, టైమ్ సిరీస్, మరియు రీఇన్ఫోర్స్మెంట్ లెర్నింగ్ను కవర్ చేసే 26 పాఠాలు - క్విజ్ అప్లికేషన్: Vue.js ఆధారిత క్విజ్ యాప్,…
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Ito ay Machine Learning para sa mga Baguhan, isang komprehensibong kurikulum na tumatagal ng 12 linggo at may 26 na aralin na sumasaklaw sa mga klasikong konsepto ng machine learning gamit ang Python (pangunahing gamit ang Scikit-learn) at R. Ang repositoryo ay idinisenyo bilang isang self-paced na mapagkukunan ng pag-aaral na may mga hands-on na proyekto, pagsusulit, at mga takdang-aralin. Ang bawat aralin ay nag-eeksplora ng mga konsepto ng ML gamit ang mga totoong datos mula sa iba't ibang kultura…
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Bu, Yeni Başlayanlar için Makine Öğrenimi adlı, Python (özellikle Scikit-learn) ve R kullanarak klasik makine öğrenimi kavramlarını kapsayan 12 haftalık, 26 derslik kapsamlı bir müfredattır. Depo, kendi hızınızda öğrenebileceğiniz, uygulamalı projeler, testler ve ödevler içeren bir kaynak olarak tasarlanmıştır. Her ders, dünya çapındaki farklı kültürlerden ve bölgelerden alınan gerçek dünya verileriyle ML kavramlarını keşfeder. Ana bileşenler: - Eğitim İçeriği: ML'ye giriş, regresyon, sınıflandırma, kümeleme, NLP, zaman serileri ve pekiştirmeli öğrenmeyi kapsayan 26 ders - Test Uygulaması: Ders öncesi ve sonrası…
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Це Machine Learning for Beginners — комплексна навчальна програма на 12 тижнів, що складається з 26 уроків, які охоплюють класичні концепції машинного навчання за допомогою Python (переважно з використанням Scikit-learn) та R. Репозиторій створений як ресурс для самостійного навчання з практичними проєктами, тестами та завданнями. Кожен урок досліджує концепції ML через реальні дані з різних культур і регіонів світу. Основні компоненти: - Навчальний контент: 26 уроків, що охоплюють введення в ML, регресію, класифікацію, кластеризацію, NLP, часові ряди та навчання з…
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یہ مشین لرننگ فار بیگنرز ہے، ایک جامع 12 ہفتوں کا، 26 اسباق پر مشتمل نصاب جو Python (زیادہ تر Scikit-learn کے ساتھ) اور R کا استعمال کرتے ہوئے کلاسک مشین لرننگ کے تصورات کا احاطہ کرتا ہے۔ یہ ریپوزٹری خود سے سیکھنے کے وسائل کے طور پر ڈیزائن کی گئی ہے، جس میں عملی پروجیکٹس، کوئزز، اور اسائنمنٹس شامل ہیں۔ ہر سبق حقیقی دنیا کے ڈیٹا کے ذریعے مختلف ثقافتوں اور علاقوں سے ML کے تصورات کو دریافت کرتا…
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Đây là Học máy dành cho người mới bắt đầu, một chương trình học toàn diện kéo dài 12 tuần với 26 bài học, bao gồm các khái niệm học máy cổ điển sử dụng Python (chủ yếu với Scikit-learn) và R. Kho lưu trữ được thiết kế như một tài nguyên học tập tự học với các dự án thực hành, bài kiểm tra và bài tập. Mỗi bài học khám phá các khái niệm học máy thông qua…
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https://api.browser-use.com/api/v1/openapi.json get /balance Returns the user's current API credit balance, which includes both monthly subscription credits and any additional purchased credits. Required for monitoring usage and ensuring sufficient credits for task execution. https://api.browser-use.com/api/v1/openapi.json get /me Returns a boolean value indicating if the API key is valid and the user is authenticated. https://api.browser-use.com/api/v1/openapi.json post /uploads/presigned-url Returns a presigned url for uploading a file to the user's files bucket. After uploading a file, the user can use the included_file_names field in the…
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This repository contains "AI Agents for Beginners" - a comprehensive educational course teaching everything needed to build AI Agents. The course consists of 18 lessons (numbered 00-18) covering fundamentals, design patterns, frameworks, production deployment, local/on-device agents, and security of AI agents. Key Technologies: - Python 3.12+ - Jupyter Notebooks for interactive learning - AI Frameworks: Microsoft Agent Framework (MAF) - Azure AI Services: Microsoft Foundry, Microsoft Foundry Agent Service V2 Architecture: - Lesson-based structure (00-18 directories) - Each lesson contains:…
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Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).
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Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.
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Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
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Query official Microsoft documentation to find concepts, tutorials, and code examples across Azure, .NET, Agent Framework, Aspire, VS Code, GitHub, and more. Uses Microsoft Learn MCP as the default, with Context7 and Aspire MCP for content that lives outside learn.microsoft.com.
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Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md or AGENTS.md?
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
What is a skill?
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Can I search my own team's files too?
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.