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diff --git a/translations/lt/README.md b/translations/lt/README.md
index 246a5e31f..39189f3fe 100644
--- a/translations/lt/README.md
+++ b/translations/lt/README.md
@@ -1,169 +1,178 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T14:06:44+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T16:46:12+00:00",
   "source_file": "README.md",
   "language_code": "lt"
 }
 -->
-[![GitHub licencija](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub prisidėtojai](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub licence](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributoriai](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
 [![GitHub problemos](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub pull-užklausos](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PR užklausos laukiamos](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Sveiki](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
 [![GitHub stebėtojai](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub fork'ai](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub žvaigždės](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub šakės](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub žvaigždutės](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
-### 🌐 Multi-Language Support
+### 🌐 Daugialypė kalbų palaikymas
 
-#### Supported via GitHub Action (Automated & Always Up-to-Date)
+#### Palaikoma per GitHub Action (automatizuota ir visada atnaujinta)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](./README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
+[Arabų](../ar/README.md) | [Bengalų](../bn/README.md) | [Bulgarų](../bg/README.md) | [Birmos (Mianmaras)](../my/README.md) | [Kinų (supaprastinta)](../zh/README.md) | [Kinų (tradicinė, Honkongas)](../hk/README.md) | [Kinų (tradicinė, Makao)](../mo/README.md) | [Kinų (tradicinė, Taivanas)](../tw/README.md) | [Kroatų](../hr/README.md) | [Čekų](../cs/README.md) | [Danų](../da/README.md) | [Olandų](../nl/README.md) | [Estų](../et/README.md) | [Suomių](../fi/README.md) | [Prancūzų](../fr/README.md) | [Vokiečių](../de/README.md) | [Graikų](../el/README.md) | [Hebrajų](../he/README.md) | [Hindų](../hi/README.md) | [Vengrų](../hu/README.md) | [Indoneziečių](../id/README.md) | [Italų](../it/README.md) | [Japonų](../ja/README.md) | [Kannadų](../kn/README.md) | [Korėjiečių](../ko/README.md) | [Lietuvių](./README.md) | [Malajų](../ms/README.md) | [Malajalų](../ml/README.md) | [Maratų](../mr/README.md) | [Nepalų](../ne/README.md) | [Nigerijos pidžino](../pcm/README.md) | [Norvegų](../no/README.md) | [Persų (Farsi)](../fa/README.md) | [Lenkų](../pl/README.md) | [Portugalų (Brazilija)](../br/README.md) | [Portugalų (Portugalija)](../pt/README.md) | [Pandžabų (Gurmukhi)](../pa/README.md) | [Rumunų](../ro/README.md) | [Rusų](../ru/README.md) | [Serbų (kirilica)](../sr/README.md) | [Slovakų](../sk/README.md) | [Slovėnų](../sl/README.md) | [Ispanų](../es/README.md) | [Svahilių](../sw/README.md) | [Švedų](../sv/README.md) | [Tagalogų (filipiniečių)](../tl/README.md) | [Tamilų](../ta/README.md) | [Telugų](../te/README.md) | [Tajų](../th/README.md) | [Turkų](../tr/README.md) | [Ukrainiečių](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamiečių](../vi/README.md)
+
+> **Norite klonuoti vietoje?**
+
+> Šiame saugykloje yra 50+ kalbų vertimų, dėl ko žymiai padidėja atsisiuntimo dydis. Norėdami klonuoti be vertimų, naudokite retą atsisiuntimą (sparse checkout):
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> Tai suteikia viską, ko reikia kursui atlikti, žymiai greičiau atsisiunčiant.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### Join Our Community
+#### Prisijunkite prie mūsų bendruomenės
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Mes turime tęstinę Discord renginių seriją „Mokymasis su AI“ — sužinokite daugiau ir prisijunkite prie [Serija „Learn with AI“](https://aka.ms/learnwithai/discord) nuo 2025 m. rugsėjo 18 d. iki 30 d. Gausite patarimų ir triukų, kaip naudoti GitHub Copilot Duomenų mokslui.
+Turime vykstančią Discord „mokymosi su AI“ seriją, daugiau sužinokite ir prisijunkite prie mūsų adresu [Learn with AI Series](https://aka.ms/learnwithai/discord) rugsėjo 18 - 30 d., 2025 m. Gaunate patarimus ir gudrybes, kaip naudoti GitHub Copilot duomenų mokslui.
 
-![Serija „Mokymasis su AI“](../../translated_images/lt/3.9b58fd8d6c373c20.webp)
+![Mokymosi su AI serija](../../../../translated_images/lt/3.9b58fd8d6c373c20.webp)
 
-# Mašininis mokymasis pradedantiesiems - mokymo programa
+# Mašininis mokymasis pradedantiesiems – mokymo programa
 
-> 🌍 Keliaukite po pasaulį, kol nagrinėsime mašininį mokymąsi per pasaulio kultūras 🌍
+> 🌍 Keliaukite po pasaulį, tyrinėdami mašininį mokymąsi per pasaulio kultūras 🌍
 
-Microsoft Cloud Advocates džiaugiasi galėdami pasiūlyti 12 savaičių, 26 pamokų mokymo programą apie **Mašininį mokymąsi**. Šioje programoje sužinosite apie tai, ką kartais vadiname **klasikiniu mašininiu mokymusi**, daugiausia naudojant Scikit-learn biblioteką ir vengiant giluminio mokymosi, kuris aptariamas mūsų [„AI pradedantiesiems“ programoje](https://aka.ms/ai4beginners). Šias pamokas taip pat derinkite su mūsų [„Duomenų mokslas pradedantiesiems“ programa](https://aka.ms/ds4beginners).
+„Microsoft“ Cloud Advocates maloniai siūlo 12 savaičių, 26 pamokų mokymo programą, skirtą **Mašininiam mokymuisi**. Šioje programoje sužinosite apie tai, ką kartais vadiname **klasikiniu mašininiu mokymusi**, daugiausia naudodami biblioteka Scikit-learn ir vengdami giluminio mokymosi, kuris aptariamas mūsų [AI pradedantiesiems mokymo programoje](https://aka.ms/ai4beginners). Derinkite šias pamokas su mūsų ['Duomenų mokslas pradedantiesiems' mokymo programa](https://aka.ms/ds4beginners).
 
-Keliauokite su mumis po pasaulį, kol taikome šias klasikines technikas duomenims iš įvairių pasaulio regionų. Kiekviena pamoka apima priešpamokinį ir popamokinį testus, rašytines instrukcijas pamokai atlikti, sprendimą, užduotį ir daugiau. Mūsų projektinė pedagogika leidžia mokytis statant projektus — patikrintas būdas, kad nauji įgūdžiai „įsilaikytų“.
+Keliaukite su mumis po pasaulį, taikydami šias klasikines technikas duomenims iš įvairių pasaulio regionų. Kiekvienoje pamokoje yra priešpamokinis ir popamokinis testai, rašytinės pamokos vykdymo instrukcijos, sprendimas, užduotis ir daugiau. Mūsų projektinė pedagogika leidžia mokytis kuriant, kas įrodytas būdas, kaip nauji įgūdžiai geriau įsitvirtina.
 
-**✍️ Nuoširdus ačiū mūsų autoriams** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ir Amy Boyd
+**✍️ Nuoširdžiai dėkojame mūsų autoriams** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ir Amy Boyd
 
-**🎨 Taip pat dėkojame iliustratoriams** Tomomi Imura, Dasani Madipalli ir Jen Looper
+**🎨 Taip pat dėkojame mūsų iliustratoriams** Tomomi Imura, Dasani Madipalli ir Jen Looper
 
-**🙏 Ypatingas ačiū 🙏 mūsų Microsoft Studentų Ambasadoriams autoriams, recenzentams ir turinio bendradarbiams**, ypač Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ir Snigdha Agarwal
+**🙏 Ypatingas ačiū 🙏 mūsų Microsoft Student Ambassador autoriams, recenzentams ir turinio dalintojams**, ypač Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ir Snigdha Agarwal
 
-**🤩 Papildomas dėkui Microsoft Studentų Ambasadoriams Eric Wanjau, Jasleen Sondhi ir Vidushi Gupta už mūsų R pamokas!**
+**🤩 Dėkojame Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi ir Vidushi Gupta už mūsų R pamokas!**
 
 # Pradžia
 
 Atlikite šiuos veiksmus:
-1. **Fork the Repository**: Spustelėkite mygtuką "Fork" šio puslapio viršutiniame dešiniajame kampe.
-2. **Clone the Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **Atšakokite saugyklą**: spustelėkite mygtuką „Fork“ viršutiniame dešiniajame šio puslapio kampe.
+2. **Klonuokite saugyklą**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [raskite visus papildomus šios kursų medžiagos išteklius mūsų Microsoft Learn rinkinyje](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [raskite visus papildomus išteklius šiam kursui mūsų Microsoft Learn kolekcijoje](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Reikia pagalbos?** Peržiūrėkite mūsų [Gedimų taisymo gidą](TROUBLESHOOTING.md) dėl dažniausiai pasitaikančių problemų su diegimu, nustatymais ir pamokų paleidimu sprendimų.
+> 🔧 **Reikia pagalbos?** Patikrinkite mūsų [Trikčių šalinimo vadovą](TROUBLESHOOTING.md) dėl dažnų problemų sprendimo diegiant, nustatant ir paleidžiant pamokas.
 
 
-**[Studentai](https://aka.ms/student-page)**, norėdami naudotis šia mokymo programa, susikurkite visą saugyklos forką savo GitHub paskyroje ir atlikite pratimus savarankiškai arba grupėje:
+**[Studentai](https://aka.ms/student-page)**, norėdami naudoti šią mokymo programą, atšakokite visą saugyklą į savo GitHub paskyrą ir vykdykite pratimus patys arba grupėje:
 
 - Pradėkite nuo priešpamokinio testo.
-- Perskaitykite paskaitą ir atlikite veiklas, sustodami ir apmąstydami kiekvieną žinių patikrinimą.
-- Stenkitės sukurti projektus suprasdami pamokas, o ne tiesiog paleisdami sprendimo kodą; vis dėlto šis kodas yra prieinamas aplankuose `/solution` kiekvienoje projektinei pamokai skirtame aplanke.
+- Skaitykite paskaitą ir atlikite veiklas, sustodami ir apmąstydami kiekvieną žinių patikrinimą.
+- Stenkitės sukurti projektus suprasdami pamokas, o ne vykdydami sprendimų kodą; tačiau šis kodas yra prieinamas kiekvieno orientuoto į projektą modulio `/solution` aplankuose.
 - Atlikite popamokinį testą.
 - Atlikite iššūkį.
 - Atlikite užduotį.
-- Baigę pamokų grupę, apsilankykite [Diskusijų lentoje](https://github.com/microsoft/ML-For-Beginners/discussions) ir „mokykitės garsiai“, užpildydami tinkamą PAT rubriką. 'PAT' yra pažangos vertinimo įrankis — rubrika, kurią užpildote siekdami tolesnio mokymosi. Taip pat galite reaguoti į kitus PAT, kad mokytumėmės kartu.
+- Baigę grupę pamokų, apsilankykite [Diskusijų lentoje](https://github.com/microsoft/ML-For-Beginners/discussions) ir „mokykitės garsiai“, užpildydami atitinkamą PAT vertinimo formą. „PAT“ yra pažangos vertinimo įrankis, kurį užpildote siekdami toliau mokytis. Taip pat galite reaguoti į kitų PAT, kad mokytumėmės kartu.
 
-> Tolimesniam mokymuisi rekomenduojame šiuos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modulius ir mokymosi kelią.
+> Tolimesniam mokymuisi rekomenduojame sekti šiuos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modulius ir mokymosi kelius.
 
-**Mokytojai**, mes [pateikėme keletą pasiūlymų](for-teachers.md), kaip naudoti šią mokymo programą.
+**Mokytojai**, mes pateikėme [keletą pasiūlymų](for-teachers.md) apie tai, kaip naudoti šią mokymo programą.
 
 ---
 
-## Vaizdo įrašų peržiūros
+## Vaizdo pristatymai
 
-Kai kurios pamokos prieinamos trumpos formos vaizdo įrašais. Visus juos rasite įterptus pamokose arba [ML for Beginners grojaraštyje Microsoft Developer YouTube kanale](https://aka.ms/ml-beginners-videos), spustelėję žemiau esantį vaizdą.
+Kai kurios pamokos prieinamos trumpais vaizdo įrašais. Juos rasite įterptus tiesiai pamokose arba [ML pradedantiesiems grojaraščio „Microsoft Developer“ YouTube kanale](https://aka.ms/ml-beginners-videos) spustelėję žemiau esantį paveikslėlį.
 
-[![ML for beginners banner](../../translated_images/lt/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML pradedantiesiems antraštė](../../../../translated_images/lt/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Susipažinkite su komanda
+## Komanda
 
 [![Reklaminis vaizdo įrašas](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
 **Gif sukūrė** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Spustelėkite aukščiau esantį paveikslėlį, kad pamatytumėte vaizdo įrašą apie projektą ir žmones, kurie jį sukūrė!
+> 🎥 Spustelėkite viršuje esantį paveikslėlį, kad pamatytumėte vaizdo įrašą apie projektą ir žmones, kurie jį sukūrė!
 
 ---
 
 ## Pedagogika
 
-Kurdami šią mokymo programą pasirinkome du pedagoginius principus: užtikrinti, kad ji būtų praktiška ir orientuota į projektus, bei kad joje būtų **dažni testai**. Be to, ši programa turi bendrą **temą**, kuri suteikia vientisumą.
+Kuriant šią mokymo programą pasirinkome du pedagoginius principus: užtikrinti, kad ji būtų praktinė, **projektinė** ir kad būtų įtraukti **dažni testai**. Be to, ši programa turi bendrą **temą**, suteikiančią nuoseklumą.
 
-Uždavus turinį susieti su projektais, procesas tampa įdomesnis studentams, o koncepcijų įsisavinimas didėja. Be to, mažos reikšmės testas prieš pamoką nukreipia studentą mokytis temos, o antras testas po pamokos užtikrina tolesnį įsiminimą. Ši mokymo programa sukurta būti lanksti ir smagi, ją galima atlikti visą arba dalimis. Projektai prasideda nuo mažų užduočių ir tampa nuosekliai sudėtingesni per 12 savaičių ciklą. Programoje taip pat yra postscriptas apie realaus pasaulio ML taikymus, kurį galima naudoti kaip papildomą užduotį arba kaip diskusijų pagrindą.
+Užtikrinus, kad turinys yra susietas su projektais, procesas tampa labiau įtraukiantis studentams ir gerėja koncepcijų įsisavinimas. Be to, mažos rizikos testas prieš pamoką nukreipia studentą į tam tikro dalyko mokymąsi, o antras testas po pamokos užtikrina tolesnį įsisavinimą. Ši programa sukurta būti lanksti ir smagi, ją galima atlikti visą arba dalimis. Projektai prasideda nuo paprastų ir palaipsniui tampa sudėtingesni per 12 savaičių ciklą. Šioje programoje taip pat yra priedas apie realius ML taikymus, kurį galima naudoti kaip papildomus kreditus arba kaip pagrindą diskusijoms.
 
-> Raskite mūsų [Elgesio kodeksą](CODE_OF_CONDUCT.md), [Indėlius](CONTRIBUTING.md), [Vertimus](TRANSLATIONS.md) ir [Gedimų taisymo](TROUBLESHOOTING.md) gaires. Laukiame jūsų konstruktyvaus atsiliepimo!
+> Susipažinkite su mūsų [Elgesio kodeksu](CODE_OF_CONDUCT.md), [Bendradarbiavimo gairėmis](CONTRIBUTING.md), [Vertimo gairėmis](TRANSLATIONS.md) ir [Trikčių šalinimo gairėmis](TROUBLESHOOTING.md). Laukiame jūsų konstruktyvios grįžtamosios reakcijos!
 
 ## Kiekviena pamoka apima
 
-- neprivalomas sketchnote
-- neprivalomas papildomas vaizdo įrašas
-- vaizdo įrašo perėjimas (kai kurios pamokos tik)
-- [priešpamokinis pasiruošimo testas](https://ff-quizzes.netlify.app/en/ml/)
-- rašytinė pamoka
-- projektinėse pamokose — žingsnis po žingsnio vadovai, kaip sukurti projektą
-- žinių patikrinimai
-- iššūkis
-- papildoma literatūra
-- užduotis
-- [popamokinis testas](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Pastaba apie kalbas**: šios pamokos daugiausia parašytos Python kalba, tačiau daugelis jų taip pat prieinamos R. Norėdami atlikti R pamoką, eikite į aplanką `/solution` ir ieškokite R pamokų. Jos turi `.rmd` plėtinį, kuris reiškia **R Markdown** failą, kurį galima apibrėžti kaip `kodo blokų` (R arba kitų kalbų) ir `YAML antraštės` (kuri nurodo, kaip formatuoti išvestis, pvz., PDF) įdėjimą į `Markdown dokumentą`. Tokiu būdu tai yra puiki autoriaus priemonė duomenų mokslui, nes leidžia sujungti kodą, jo rezultatus ir pastabas, užrašant jas Markdown formatu. Be to, R Markdown dokumentai gali būti sugeneruoti į tokias išvesties formas kaip PDF, HTML ar Word.
-
-> **Pastaba apie testus**: visi testai yra [Quiz App aplanke](../../quiz-app), iš viso 52 testai po tris klausimus. Juos galima pasiekti pamokose, bet quiz app galima paleisti ir vietoje; sekite instrukcijas `quiz-app` aplanke, kad vietoje paleistumėte arba diegtumėte į Azure.
-
-| Pamokos numeris |                             Tema                              |                   Pamokų grupė                   | Mokymosi tikslai                                                                                                             |                                                              Susijusi pamoka                                                               |                        Autorius                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Įvadas į mašininį mokymąsi                |      [Įvadas](1-Introduction/README.md)       | Sužinokite pagrindines mašininio mokymosi sąvokas                                                                                |                                             [Pamoka](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Mašininio mokymosi istorija                 |      [Įvadas](1-Introduction/README.md)       | Sužinokite šios srities istoriją                                                                                         |                                            [Pamoka](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen ir Amy                      |
-|      03       |                 Teisingumas ir mašininis mokymasis                  |      [Įvadas](1-Introduction/README.md)       | Kokie svarbūs filosofiniai klausimai apie teisingumą, kuriuos studentai turėtų apsvarstyti kurdami ir taikydami ML modelius? |                                              [Pamoka](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Mašininio mokymosi technikos                 |      [Įvadas](1-Introduction/README.md)       | Kokių technikų ML tyrėjai naudoja kuriant ML modelius?                                                                       |                                          [Pamoka](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris ir Jen                     |
-|      05       |                   Įvadas į regresiją                   |        [Regresija](2-Regression/README.md)         | Susipažinkite su Python ir Scikit-learn, skirtais regresijos modeliams                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Šiaurės Amerikos moliūgų kainos 🎃                |        [Regresija](2-Regression/README.md)         | Vizualizuokite ir išvalykite duomenis pasiruošimui ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Šiaurės Amerikos moliūgų kainos 🎃                |        [Regresija](2-Regression/README.md)         | Sukurkite linijinius ir polinominius regresijos modelius                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen ir Dmitry • Eric Wanjau       |
-|      08       |                Šiaurės Amerikos moliūgų kainos 🎃                |        [Regresija](2-Regression/README.md)         | Sukurkite logistinės regresijos modelį                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Žiniatinklio programa 🔌                          |           [Žiniatinklio programa](3-Web-App/README.md)            | Sukurkite žiniatinklio programą, skirtą naudoti jūsų apmokytą modelį                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Įvadas į klasifikaciją                 |    [Klasifikacija](4-Classification/README.md)     | Išvalykite, paruoškite ir vizualizuokite savo duomenis; įvadas į klasifikaciją                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen ir Cassie • Eric Wanjau |
-|      11       |             Skani Azijos ir Indijos virtuvė 🍜             |    [Klasifikacija](4-Classification/README.md)     | Įvadas į klasifikatorius                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ir Cassie • Eric Wanjau |
-|      12       |             Skani Azijos ir Indijos virtuvė 🍜             |    [Klasifikacija](4-Classification/README.md)     | Daugiau klasifikatorių                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ir Cassie • Eric Wanjau |
-|      13       |             Skani Azijos ir Indijos virtuvė 🍜             |    [Klasifikacija](4-Classification/README.md)     | Sukurkite rekomendacinę žiniatinklio programą, naudodami savo modelį                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Įvadas į klasterizaciją                   |        [Klasterizacija](5-Clustering/README.md)         | Išvalykite, paruoškite ir vizualizuokite savo duomenis; įvadas į klasterizaciją                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Nigerijos muzikos skonių tyrinėjimas 🎧              |        [Klasterizacija](5-Clustering/README.md)         | Ištirkite K-Means klasterizacijos metodą                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Įvadas į natūralios kalbos apdorojimą ☕️         |   [Natūralios kalbos apdorojimas](6-NLP/README.md)    | Sužinokite NLP pagrindus kurdami paprastą botą                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Bendros NLP užduotys ☕️                      |   [Natūralios kalbos apdorojimas](6-NLP/README.md)    | Pagilinkite savo NLP žinias suprasdami dažniausiai pasitaikančias užduotis, susijusias su kalbos struktūromis                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Vertimas ir nuotaikų analizė ♥️              |   [Natūralios kalbos apdorojimas](6-NLP/README.md)    | Vertimas ir nuotaikų analizė su Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Romantiški viešbučiai Europoje ♥️                  |   [Natūralios kalbos apdorojimas](6-NLP/README.md)    | Nuotaikų analizė su viešbučių atsiliepimais 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Romantiški viešbučiai Europoje ♥️                  |   [Natūralios kalbos apdorojimas](6-NLP/README.md)    | Nuotaikų analizė su viešbučių atsiliepimais 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Įvadas į laiko eilučių prognozavimą             |        [Laiko eilutės](7-TimeSeries/README.md)        | Įvadas į laiko eilučių prognozavimą                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Pasaulio elektros suvartojimas ⚡️ - laiko eilučių prognozavimas su ARIMA |        [Laiko eilutės](7-TimeSeries/README.md)        | Laiko eilučių prognozavimas su ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Pasaulio elektros suvartojimas ⚡️ - laiko eilučių prognozavimas su SVR  |        [Laiko eilutės](7-TimeSeries/README.md)        | Laiko eilučių prognozavimas su Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Įvadas į stiprinamąjį mokymą             | [Stiprinamasis mokymasis](8-Reinforcement/README.md) | Įvadas į stiprinamąjį mokymą naudojant Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Padėkite Peteriui išvengti vilko! 🐺                  | [Stiprinamasis mokymasis](8-Reinforcement/README.md) | Stiprinamojo mokymosi Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postskriptas   |            Tikro pasaulio ML scenarijai ir taikymai            |      [ML laukinėje aplinkoje](9-Real-World/README.md)       | Įdomios ir atskleidžiančios tikroviškos klasikinio ML taikymo sritys                                                               |                                             [Pamoka](9-Real-World/1-Applications/README.md)                                              |                         Komanda                         |
-|  Postskriptas   |            Modelių derinimas ML naudojant RAI skydelį          |      [ML laukinėje aplinkoje](9-Real-World/README.md)       | Modelių derinimas mašininio mokymosi srityje naudojant Responsible AI skydelio komponentus                                                              |                                             [Pamoka](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+- pasirenkamą eskizo užrašą
+- pasirenkamą papildomą vaizdo įrašą
+- vaizdo peržiūrą (kai kurios pamokos)
+- [priešpamokinį įšilimo testą](https://ff-quizzes.netlify.app/en/ml/)
+- rašytines pamokas
+- projektinėse pamokose – žingsnis po žingsnio gidus, kaip sukurti projektą
+- žinių patikrinimus
+- iššūkį
+- papildomą skaitymą
+- užduotį
+- [popamokinį testą](https://ff-quizzes.netlify.app/en/ml/)
+
+> **Pastaba apie kalbas**: Šios pamokos daugiausia rašomos Python kalba, tačiau dauguma jų yra prieinamos ir R kalba. Norėdami atlikti R pamoką, eikite į `/solution` aplanką ir raskite R pamokas. Jos turi .rmd plėtinį, kuris reiškia **R Markdown** failą, kurį galima apibūdinti kaip `kodo blokų` (R ar kitų kalbų) ir `YAML antraštės` (nurodančios, kaip formatuoti išvestis, pvz., PDF) įterpimą `Markdown dokumente`. Todėl tai puiki autorinė sistema duomenų mokslui, leidžianti derinti savo kodą, išvestį ir mintis rašant Markdown forma. Be to, R Markdown dokumentus galima konvertuoti į įvairius išvesties formatus, tokius kaip PDF, HTML ar Word.
+> **Pastaba apie viktorinas**: Visos viktorinos yra [Quiz App kataloge](../../quiz-app), iš viso 52 viktorinos po tris klausimus kiekvienoje. Jos sujungtos pamokų viduje, tačiau viktorinų programėlę galite paleisti vietoje; vadovaukitės instrukcijomis `quiz-app` kataloge, kad paleistumėte vietoje arba išplatintumėte Azure.
+
+| Pamokos numeris |                             Temos                              |                   Pamokų grupė                   | Mokymosi tikslai                                                                                                              |                                                              Susieta pamoka                                                               |                        Autorius                        |
+| :-------------: | :-----------------------------------------------------------: | :----------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|       01        |                Įvadas į mašininį mokymąsi                   |      [Įvadas](1-Introduction/README.md)          | Susipažinkite su pagrindinėmis mašininio mokymosi sąvokomis                                                                     |                                             [Pamoka](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|       02        |                Mašininio mokymosi istorija                   |      [Įvadas](1-Introduction/README.md)          | Sužinokite šios srities istoriją                                                                                               |                                            [Pamoka](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen ir Amy                        |
+|       03        |                 Teisingumas ir mašininis mokymasis           |      [Įvadas](1-Introduction/README.md)          | Kokie yra svarbūs filosofiniai klausimai apie teisingumą, kuriuos studentai turėtų apsvarstyti kuriant ir taikant ML modelius?  |                                              [Pamoka](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|       04        |                Mašininio mokymosi metodai                    |      [Įvadas](1-Introduction/README.md)          | Kokius metodus ML tyrėjai naudoja ML modeliams kurti?                                                                          |                                          [Pamoka](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris ir Jen                      |
+|       05        |                   Įvadas į regresiją                         |        [Regresija](2-Regression/README.md)        | Pradėkite dirbti su Python ir Scikit-learn regresijos modeliams                                                                |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|       06        |                Šiaurės Amerikos moliūgų kainos 🎃            |        [Regresija](2-Regression/README.md)        | Vaizduokite ir valykite duomenis pasiruošimui ML                                                                                |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|       07        |                Šiaurės Amerikos moliūgų kainos 🎃            |        [Regresija](2-Regression/README.md)        | Kurkite tiesinės ir polinomų regresijos modelius                                                                               |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen ir Dmitry • Eric Wanjau       |
+|       08        |                Šiaurės Amerikos moliūgų kainos 🎃            |        [Regresija](2-Regression/README.md)        | Kurkite logistinės regresijos modelį                                                                                            |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|       09        |                          Interneto programėlė 🔌             |           [Web App](3-Web-App/README.md)           | Sukurkite interneto programėlę panaudodami savo paruoštą modelį                                                                 |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|       10        |                 Įvadas į klasifikaciją                       |    [Klasifikacija](4-Classification/README.md)    | Valykite, paruoškite ir vizualizuokite duomenis; įvadas į klasifikaciją                                                         | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen ir Cassie • Eric Wanjau |
+|       11        |             Gardūs Azijos ir Indijos patiekalai 🍜           |    [Klasifikacija](4-Classification/README.md)    | Įvadas į klasifikatorius                                                                                                       | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ir Cassie • Eric Wanjau |
+|       12        |             Gardūs Azijos ir Indijos patiekalai 🍜           |    [Klasifikacija](4-Classification/README.md)    | Daugiau klasifikatorių                                                                                                         | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ir Cassie • Eric Wanjau |
+|       13        |             Gardūs Azijos ir Indijos patiekalai 🍜           |    [Klasifikacija](4-Classification/README.md)    | Sukurkite rekomendacinę interneto programėlę naudodami savo modelį                                                              |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|       14        |                   Įvadas į grupavimą                         |        [Grupavimas](5-Clustering/README.md)         | Valykite, paruoškite ir vizualizuokite duomenis; Įvadas į grupavimą                                                             |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|       15        |              Nigerijos muzikos skonio tyrinėjimas 🎧         |        [Grupavimas](5-Clustering/README.md)         | Tyrinėkite K-vidurkių grupavimo metodą                                                                                         |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|       16        |        Įvadas į natūralios kalbos apdorojimą ☕️             |   [Natūralios kalbos apdorojimas](6-NLP/README.md) | Sužinokite NLP pagrindus kurdami paprastą botą                                                                                 |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|       17        |                      Dažnos NLP užduotys ☕️                 |   [Natūralios kalbos apdorojimas](6-NLP/README.md) | Gilinkite NLP žinias suprasdami dažnas užduotis, susijusias su kalbos struktūromis                                               |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|       18        |             Vertimas ir nuotaikų analizė ♥️                  |   [Natūralios kalbos apdorojimas](6-NLP/README.md) | Vertimo ir nuotaikų analizė su Jane Austen                                                                                      |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|       19        |                  Romantiški Europos viešbučiai ♥️            |   [Natūralios kalbos apdorojimas](6-NLP/README.md) | Nuotaikų analizė su viešbučių atsiliepimais 1                                                                                   |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|       20        |                  Romantiški Europos viešbučiai ♥️            |   [Natūralios kalbos apdorojimas](6-NLP/README.md) | Nuotaikų analizė su viešbučių atsiliepimais 2                                                                                   |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|       21        |            Įvadas į laiko eilučių prognozavimą               |        [Laiko eilutės](7-TimeSeries/README.md)      | Įvadas į laiko eilučių prognozavimą                                                                                            |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|       22        | ⚡️ Pasaulinis energijos naudojimas ⚡️ - laiko eilučių prognozavimas su ARIMA |        [Laiko eilutės](7-TimeSeries/README.md)      | Laiko eilučių prognozavimas su ARIMA                                                                                           |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|       23        |  ⚡️ Pasaulinis energijos naudojimas ⚡️ - laiko eilučių prognozavimas su SVR  |        [Laiko eilutės](7-TimeSeries/README.md)      | Laiko eilučių prognozavimas su paramos vektorių regresoriumi                                                                   |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|       24        |             Įvadas į stiprinamąjį mokymąsi                  | [Stiprinamasis mokymasis](8-Reinforcement/README.md) | Įvadas į stiprinamąjį mokymąsi naudojant Q-mokymą                                                                               |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|       25        |                 Padėkite Peteriui išvengti vilko! 🐺          | [Stiprinamasis mokymasis](8-Reinforcement/README.md) | Stiprinamojo mokymosi treniruoklis                                                                                            |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  Postscript    |            Tikri mašininio mokymosi scenarijai ir taikymai   |      [ML laukinėje gamtoje](9-Real-World/README.md) | Įdomios ir atskleidžiančios tikrojo pasaulio klasikinio ML taikymo sritys                                                      |                                             [Pamoka](9-Real-World/1-Applications/README.md)                                              |                         Komanda                       |
+|  Postscript    |            Modelių derinimas ML naudojant RAI skydelį         |      [ML laukinėje gamtoje](9-Real-World/README.md) | Modelių derinimas mašininiame mokymesi naudojant Atsakingo AI skydelio komponentus                                              |                                             [Pamoka](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                    |
 
 > [raskite visus papildomus šio kurso išteklius mūsų Microsoft Learn kolekcijoje](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-## Prieiga neprisijungus
+## Offline prieiga
 
-You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
+Šią dokumentaciją galite paleisti neprisijungę, naudodami [Docsify](https://docsify.js.org/#/). Šaknikite šį repozitorijų, [įdiekite Docsify](https://docsify.js.org/#/quickstart) savo vietiniame kompiuteryje, o tada šio repozitoriumo šakniniame kataloge įveskite `docsify serve`. Svetainė bus pateikta per 3000 prievadą jūsų localhost adresu: `localhost:3000`.
 
 ## PDF failai
 
-Find a pdf of the curriculum with links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+Raskite mokymo programos PDF su nuorodomis [čia](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
 ## 🎒 Kiti kursai 
 
-Mūsų komanda rengia kitus kursus! Peržiūrėkite:
+Mūsų komanda kuria ir kitus kursus! Peržiūrėkite:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -181,37 +190,37 @@ Mūsų komanda rengia kitus kursus! Peržiūrėkite:
 ---
  
 ### Generatyvinio AI serija
-[![Generatyvinis AI pradedantiesiems](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Generatyvinis AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![Generatyvusis DI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![Generatyvusis DI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![Generatyvinis DI pradedantiesiems](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generatyvinis DI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+[![Generatyvinis DI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generatyvinis DI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Pagrindiniai mokymai
-[![Mašininis mokymasis pradedantiesiems](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+### Pagrindinis mokymasis
+[![ML pradedantiesiems](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
 [![Duomenų mokslas pradedantiesiems](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
 [![DI pradedantiesiems](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
 [![Kibernetinis saugumas pradedantiesiems](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![Tinklapių kūrimas pradedantiesiems](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![Interneto svetainių kūrimas pradedantiesiems](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
 [![Daiktų internetas pradedantiesiems](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
 [![XR kūrimas pradedantiesiems](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Copilot serija
-[![Copilot skirtas DI poriniam programavimui](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![Copilot skirtas C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot dirbant kartu su DI programavimas](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
 [![Copilot nuotykiai](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## Pagalba
+## Pagalbos gavimas
 
-Jei užstrigote arba turite klausimų apie DI programėlių kūrimą, prisijunkite prie kitų besimokančiųjų ir patyrusių kūrėjų diskusijų apie MCP. Tai palaikanti bendruomenė, kurioje klausimai yra laukiami, o žinios laisvai dalijamos.
+Jei įstringate ar turite klausimų apie DI programėlių kūrimą. Prisijunkite prie bendraminčių mokinių ir patyrusių programuotojų diskusijose apie MCP. Tai palaikanti bendruomenė, kurioje klausimai laukiami, o žinios laisvai dalijamos.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Jei turite atsiliepimų apie produktą arba radote klaidų kūrimo metu, apsilankykite:
+Jei turite produkto atsiliepimų ar klaidų kūrimo metu, apsilankykite:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
@@ -219,5 +228,5 @@ Jei turite atsiliepimų apie produktą arba radote klaidų kūrimo metu, apsilan
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
 **Atsakomybės apribojimas**:
-Šis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, atkreipkite dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas originalioje kalboje turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojame naudoti profesionalų, žmogaus atliktą vertimą. Mes neatsakome už jokius nesusipratimus ar neteisingas interpretacijas, kylančias dėl šio vertimo naudojimo.
+Šis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatizuoti vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojamas profesionalus žmogaus vertimas. Mes nesame atsakingi už bet kokius nesusipratimus ar neteisingus aiškinimus, kylančius dėl šio vertimo naudojimo.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/my/README.md b/translations/my/README.md
index ac3b30220..fc0f65445 100644
--- a/translations/my/README.md
+++ b/translations/my/README.md
@@ -1,168 +1,176 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T13:52:24+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T16:41:46+00:00",
   "source_file": "README.md",
   "language_code": "my"
 }
 -->
-[![GitHub လိုင်စင်](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub ပါဝင်သူများ](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub Issue များ](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs ကြိုဆိုပါသည်](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub ကြည့်ရှုသူများ](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub Forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub ကြယ်များ](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
-### 🌐 ဘာသာစကားစုံထောက်ပံ့မှု
+### 🌐 ဘာသာစကားအမျိုးမျိုးအတွက် ပံ့ပိုးမှု
 
-#### GitHub Action မှတဆင့် ထောက်ပံ့ (အလိုအလျောက်နှင့် အမြဲတမ်း နောက်ဆုံးပေါ်)
+#### GitHub Action ဖြင့် ပံ့ပိုးထားသည် ( အလိုအလျောက်နှင့် အမြဲအသစ်တင်ထားသည် )
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
 [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](./README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
+
+> **ဒေသတွင်း ကလုန်းရှင် ဆိုပါက?**
+
+> ဒီ GitHub repository မှာ ဘာသာစကား ၅၀ ကျော်ရည်ရွယ်ချက်များပါရှိသောကြောင့် ဒေါင်းလုပ်အရွယ်အစား အများကြီးမြင့်တက်သည်။ ဘာသာပြန်လိုတာမပါဘဲ clone ချင်ရင် sparse checkout ကို အသုံးပြုပါ:
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> ဒီအတိုင်း ပြီးမြောက်အောင် လေ့လာပြီး လျင်မြန်တဲ့ ဒေါင်းလုပ် ရယူနိုင်ပါမယ်။
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### ကျွန်တော်တို့အသိုင်းအဝိုင်းထဲ ဆက်သွယ်ပါ
+#### ကျွန်ုပ်တို့၏ သမိုင်းပတ်လမ်းဝင် ကွန်ယက်တွင် ဝင်ရောက်ပါ
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ကျွန်တော်တို့မှာ Discord ပေါ်တွင် "Learn with AI" စီးရီးတစ်ခု ဆက်လက်လည်ပတ်နေပါသည်။ 18 - 30 September, 2025 အတွင်း၊ အသေးစိတ်ကို သင်ယူရန်နှင့် ပူးပေါင်းရန် [Learn with AI Series](https://aka.ms/learnwithai/discord) တွင် ပါဝင်နိုင်ပါတယ်။ GitHub Copilot ကို Data Science အတွက် အသုံးပြုနည်း လှမ်းများနှင့် ကိုယ့်ရဲ့လက်တွေ့အကောင်းဆုံး လက်နက်များ၊ လက်ရာများကို ရယူနိုင်ပါမယ်။
+AI နဲ့ သင်ယူခြင်း စီးရီးကို Discord မှာ ဆက်လက်ပြုလုပ်နေပြီး၊ [Learn with AI Series](https://aka.ms/learnwithai/discord) မှာ ၂၀၂၅ ခုနှစ် စက်တင်ဘာလ ၁၈ ရက်မှ ၃၀ ရက်အတွင်း ပိုပြီးသိရှိတွေ့ရှိနိုင်ပါသည်။ GitHub Copilot ကို Data Science အတွက် ဘယ်လိုအသုံးချရမလဲ ဆွေးနွေးပေးသည်။
 
-![AI နှင့် လေ့လာမှု စီးရီး](../../translated_images/my/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/my/3.9b58fd8d6c373c20.webp)
 
-# မစတင်သူများအတွက် Machine Learning - သင်ရိုးညွှန်းတမ်း
+# စတင်သင်ယူရန် Machine Learning for Beginners - သင်ရိုးညွှန်းတမ်း
 
-> 🌍 ကမ္ဘာတစ်ဝှမ်း ခရီးထွက်ပြီး ကမ္ဘာ့ယဉ်ကျေးမှုများအား အသုံးချ၍ Machine Learning ကို လေ့လာကြမယ် 🌍
+> 🌍 အပြည်ပြည်ဆိုင်ရာ ယဉ်ကျေးမှုများဖြင့် Machine Learning ကို ခရီးဆက်သွားခြင်း။ 🌍
 
-Cloud Advocates တွေက Microsoft မှ အသင်းအဖွဲ့သုံး 12 ပတ်၊ 26 သင်ခန်းစာပါအောင် **Machine Learning** အကြောင်း အားလုံးပါရှိတဲ့ သင်ရိုးအစီအစဉ်တစ်ခု ကို ပံ့ပိုးပေးလိုက်ပါတယ်။ ယခု သင်ရိုးတွင် အဓိကအားဖြင့် Scikit-learn ကို အသုံးပြုပြီး အတက်ရောက်သော deep learning ကို ရှောင်ရှားကာ (deep learning ကို ကျွန်တော်တို့ရဲ့ [AI for Beginners' curriculum](https://aka.ms/ai4beginners) တွင် ဖော်ပြထားသည်) သင်ကြားပေးထားပါတယ်။ ဒီသင်ခန်းစာများကို ကျွန်တော်တို့ရဲ့ ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) နှင့်တွဲဖက်၍လည်း အသုံးပြုနိုင်ပါတယ်။
+Microsoft ၏ Cloud Advocates နဲ့အတူ ၁၂ ပတ် ကြာ၊ ၂၆ ပုဒ်သင်ခန်းစာ အသီးသီးနှင့် ပေါင်းစပ်ထားသည့် **Machine Learning** သင်ရိုးညွှန်းတမ်းကို ပေးအပ်ခြင်းမှာ ဝမ်းမြောက်ပါသည်။ ဒီသင်ရိုးညွှန်းတမ်းမှာ သင်ကိုယ်တိုင် လေ့လာရမှာကတော့ အဓိကအားဖြင့် Scikit-learn ကို အသုံးပြုပြီး ခေတ်ပြိုင် classic machine learning ကို အခြေခံလေ့လာမည် ဖြစ်ပြီး deep learning ကိုတော့ ကျွန်တော်တို့ရဲ့ [AI for Beginners' curriculum](https://aka.ms/ai4beginners) ထဲမှာ ဖော်ပြထားပါတယ်။ ဒါ့အပြင် ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) ကိုလည်း စုံလင်စေရန် တွဲဖက် သင်ယူပါ။
 
-ကမ္ဘာတစ်ဝှမ်း ခရီးထွက်ပြီး classic technique တွေကို ကမ္ဘာ၏ အချက်အလက်များပေါ် အသုံးချကာ လေ့လာကြမယ်။ သင်ခန်းစာတိုင်းတွင် pre- နှင့် post- သင်ခန်းစာ မေးစရာများ၊ သင်ခန်းစာများကို ပြီးစီးရန် ရေးသားထားသော ညွှန်ကြားချက်များ၊ ဖြေရှင်းချက်များ၊ အာစီန်းမင့်များ နှင့် အခြား အရာများ ပါဝင်သည်။ ကျွန်တော်တို့၏ project-based သင်ကြားပေးမှုနည်းလမ်းက သင်ယူသူများကို တည်ဆောက်နေချိန်တွင် သင်ယူစေခြင်းဖြင့် ကျွမ်းကျင်မှုများ စွဲကြည်စေသည်။
+ကမ္ဘာတစ်ဝှမ်းရှိ ဒေတာများကို classic methods နည်းလမ်းတွေ လက်တွေ့အသုံးချပြီး ခရီးသွားကြမယ်။ သင်ခန်းစာတိုင်းမှာ အတန်းမတိုင်ခင် စစ်တမ်း စစ်ဆေးမှုနဲ့ အပြီးသတ် စစ်တမ်းစစ်ဆေးမှု၊ လက်တွေ့လုပ်ဆောင်ချက်ရေးညွှန်ကြားချက်များ၊ ဖြေရှင်းချက်၊ လေ့ကျင့်ခန်းနဲ့ အခြားအရာများ ပါဝင်ပါသည်။ ကျွန်တော်တို့၏ project-based သင်ကြားမှုနည်းလမ်းက နေရာတိုင်းမှာ လေ့လာပြီး တည်ဆောက်လိုက်ရတဲ့ နည်းဖြစ်တာကြောင့် အသစ်သင်ယူတဲ့ ကျွမ်းကျင်မှုတွေ ပိုမိုရှိလာဖို့ အထူးကောင်းပါတယ်။
 
-**✍️ ကျေးဇူးအများကြီး တင်ရှိပါသည် ကျွန်တော်တို့၏ ရေးသားခဲ့သူများအတွက်** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu နှင့် Amy Boyd
+**✍️ ကျေးဇူးအထူးပေးသူများ** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu နှင့် Amy Boyd
 
-**🎨 ဂရိတ်ပေးရသော ပုံဆွဲသူများကိုလည်း ကျေးဇူးတင်ရှိပါသည်** Tomomi Imura, Dasani Madipalli, နှင့် Jen Looper
+**🎨 ပုံဆွဲသူများအားလုံးကိုလည်း ကျေးဇူးတင်ပါသည်** Tomomi Imura, Dasani Madipalli, နှင့် Jen Looper
 
-**🙏 အထူးကျေးဇူးများ 🙏 Microsoft Student Ambassador အဖြစ် ရေးသားသူများ၊ ပြန်လည်သုံးသပ်သူများနှင့် မျှဝေသူများအား**, အထူးသဖြင့် Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, နှင့် Snigdha Agarwal
+**🙏 Microsoft Student Ambassador စာရေးသူများ၊ သုံးသပ်သူများနှင့် မဟာဗျူဟာ ထောက်ပံ့သူများအား အထူးကျေးဇူးတင်ပါတယ်**၊ Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, နှင့် Snigdha Agarwal
 
-**🤩 ကျွန်တော်တို့၏ R သင်ခန်းစာများအတွက် Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, နှင့် Vidushi Gupta တို့ကို ထပ်မံကျေးဇူးတင်ရှိပါသည်!**
+**🤩 Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, နှင့် Vidushi Gupta အတွက် ကျေးဇူးအထူးရှိပါတယ် ကျွန်တော်တို့ရဲ့ R သင်ခန်းစာများအတွက်!**
 
-# စတင်ရန်
+# စတင်အသုံးပြုရန်
 
-ဒီအဆင့်များကို လိုက်နာပါ:
-1. **Repository ကို Fork လုပ်ပါ**: ဒီစာမျက်နှာ၏ ထောင့်ညာ အပေါ်ဖက်ရှိ "Fork" ခလုတ်ကို နှိပ်ပါ။
-2. **Repository ကို Clone လုပ်ပါ**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+အောက်ပါ အဆင့်များကို လိုက်နာပါ-
+1. **Repository ကို Fork လုပ်ရန်** - ဒီစာမျက်နှာ၏ညာဘက်အပေါ်ထောင့်မှ "Fork" ခလုတ်ကို နှိပ်ပါ။
+2. **Repository ကို Clone လုပ်ရန်** - `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [အခုသင်ယူမည့် သင်တန်းအတွက် အပိုအရင်းအမြစ်များအားလုံးကို ကျွန်တော်တို့၏ Microsoft Learn အစုအရင်းမှ ရှာတွေ့ပါ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [ဒီသင်တန်းအတွက် အပိုအရင်းအမြစ်အားလုံးကို Microsoft Learn စုစည်းမှုမှာ ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **ကူညီလိုပါသလား?** installation, setup, နှင့် သင်ခန်းစာများ ပြေးဆွဲရာတွင် ဖြစ်တတ်သော ပုံမှန် ပြဿနာများအတွက် ဖြေရှင်းချက်များကို ကြည့်ရန် [Troubleshooting Guide](TROUBLESHOOTING.md) ကို စစ်ဆေးပါ။
+> 🔧 **အကူအညီလိုပါသလား?** မူရင်း Settings တွေ၊ အစပြုခြင်းနဲ့ သင်ခန်းစာ များအတွက် သွားရောက်ဖြေရှင်းနိုင်ဖို့ [Troubleshooting Guide](TROUBLESHOOTING.md) ကို ကြည့်ပါ။
 
-**[ကျောင်းသားများ](https://aka.ms/student-page)**၊ ဒီသင်ရိုးကို အသုံးပြုရန်အတွက် သင့် GitHub အကောင့်သို့ repository ကို အားလုံး Fork လုပ်ပြီး ကိုယ့်အနေဖြင့် သို့မဟုတ် အုပ်စုတစ်စုနှင့် အတူ လေ့ကျင့်ခန်းများကို ပြီးစီးပါ:
+**[ကျောင်းသားများ](https://aka.ms/student-page)**၊ ဒီသင်ရိုးကို အသုံးပြုရန်၊ သင့် GitHub အကောင့်သို့ ကြီးပြင်းစွာ Fork လုပ်ပြီး လေ့ကျင့်ခန်းများ ကို သင် တစ်ဦးတည်း သို့မဟုတ် အဖွဲ့လိုက် မြှင့်တင် ပြီးစီးနိုင်ပါသည်-
 
-- စတင်ရန် pre-lecture quiz ဖြေပါ။
-- သင်ခန်းစာကို ဖတ်ပြီး လှုပ်ရှားမှုများကို ပြီးစီးပါ၊ knowledge check များတွင် တစ်ခါစတင် မှတ်ယူပြီး ပြန်လှေကားပါ။
-- solution ကုဒ်ကို အလျင်အမြန် လှုပ်ချင်သလို မလှုပ်ပါ၊ သင်ခန်းစာများကို နားလည်မှုဖြင့် ပရောဂျက်များကို စမ်းသပ်ကြည့်ရန် ကြိုးစားပါ။ သို့သော် ဒီကုဒ်များကို `/solution` ဖိုလ်ဒါများတွင် project-oriented သင်ခန်းစာတစ်ခုချင်းစီအတွက် ရှိပါသည်။
-- post-lecture quiz ကို ဖြေပါ။
-- စိန်ခေါ်မှုကို ဖြေရှင်းပါ။
-- အပ်မဲ့တာဝန်(Assignment) ကို ပြီးစီးပါ။
-- သင်ခန်းစာအုပ်စုတစ်ခု ပြီးစီးပါက၊ [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) သို့ သွား၍ သင့်လေ့လာမှုကို "learn out loud" ဖြစ်အောင် သင့် PAT rubric ကို ဖြည့်စွက်ပါ။ 'PAT' သည် သင်၏ တိုးတက်မှုကို ဆန်းစစ်ရန် အသုံးပြုသော Progress Assessment Tool ဖြစ်ပြီး သင်ဖြည့်စွက်ရန် ရူဘရစ်တစ်ခုဖြစ်သည်။ အခြား PAT များကိုလည်း တုံ့ပြန်နိုင်ပြီး အတူတကွ သင်ယူကြပါစို့။
+- သင်ခန်းစာမတိုင်မီ စစ်တမ်းစစ်ဆေးမှုစတင်ပါ။
+- သင်ခန်းစာကို ဖတ်ပြီး လုပ်ဆောင်ချက်များ ပြီးစီးပါ၊ အောက်တိုဘာလို့ ပြန်ကြည့်သုံးသပ်ချက်များ သတိထားပါ။
+- သင်ခန်းစာများကို ဖြေရှင်းချက်ကို မတင်မဖြစ် မေးမြန်းရမဲ့ နည်းလမ်းအသစ်ဖြင့် ဆန်းစစ်ပြီး projects များ တည်ဆောက်ကြပါ။ ဒါပေမဲ့ solution code ကို project အခြေခံသော သင်ခန်းစာတိုင်း၏ `/solution` ဖိုလ်ဒါတွင် ရှိနေပါသည်။
+- သင်ခန်းစာပြီးပါက ကျဆုံးစစ်ပွဲယူပါ။
+- စိန်ခေါ်မှုကို ပြီးမြောက်စေပါ။
+- လက်တွေ့လေ့ကျင့်ခန်းကို ပြီးစီးပါ။
+- သင်ခန်းစာအုပ်စုတစ်စု ပြီးဆုံးစဉ်မှာ [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) သို့ သွားပြီး သင်ယူသင့်သလို “အသံထွက်လှုပ်ရှား” မှတ်ချက်များဖြည့်ပါ။ PAT rubric ဆိုသည်မှာ သင့်တိုးတက်မှု စစ်တမ်းတစ်ခုဖြစ်ပြီး သင့်သင်ယူမှုကို တိုးတက်အောင်လုပ်ပေးသည်။ မိမိအပြင်တခြား PAT များကိုလည်း တုံ့ပြန်ပေးနိုင်ပါသည်။
 
-> နောက်ထပ် သင်ကြားမှုအတွက် ကျွန်တော်တို့ အကြံပေးလိုသော [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules နှင့် learning paths များကို လိုက်နာကြည့်ပါ။
+> ပိုမိုလေ့လာလိုပါက ကျွန်တော်တို့ခွင့်ပြုတဲ့ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) အတန်းများနှင့် သင်ယူလမ်းကြောင်းများကို လိုက်နာပါ။
 
-**ဆရာ/ဆရာမများ**, ကျွန်တော်တို့မှာ ဒီသင်ရိုးကို ဘယ်လိုအသုံးချရမည်ဆိုသည့် ပေးအပ်ချက်အချို့ကို [included some suggestions](for-teachers.md) တွင် ထည့်သွင်းပေးထားသည်။
+**ဆရာ/ဆရာမများအနေနဲ့** ဒီသင်ရိုးကို အသုံးပြုနည်းအကြံပြုချက်အချို့ကို ကျွန်တော်တို့ [အောက်ပါစာမျက်နှာတွင်](for-teachers.md) ထည့်သွင်းပေးထားပါတယ်။
 
 ---
 
-## ဗီဒီယို လမ်းညွှန်မှုများ
+## ဗီဒီယို လမ်းညွှန်ချက်များ
 
-သင်ခန်းစာတချို့ကို အတိုချုံး ဗီဒီယိုအဖြစ် ရရှိနိုင်ပါသည်။ သင်ခန်းစာများထဲတွင် တန်းစီထည့်ထားသော ဗီဒီယိုများအားလုံးကို သင့်သင်ခန်းစာများအတွင်းကနေ သို့မဟုတ် [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) မှာ ဗီဒီယိုကို နှိပ်၍ ကြည့်ရှုနိုင်သည်။
+သင်ခန်းစာအချို့မှာ ဗီဒီယို အတိုချုံးအဖြစ် ရရှိနိုင်တယ်။ သင်ခန်းစာတွင်းရှိ ဗီဒီယိုများကို သို့မဟုတ် [Microsoft Developer YouTube မှ ML for Beginners playlist](https://aka.ms/ml-beginners-videos) တွင် ကြည့်ရှုနိုင်ပါသည်။
 
-[![ML for beginners banner](../../translated_images/my/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/my/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## အဖွဲ့ကို မိတ်ဆက်ခြင်း
+## အသင်းအဖွဲ့ လေးတွေ့ဆုံခြင်း
 
-[![ပရိုမိုးရှင်း ဗီဒီယို](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**Gif ရိုက်သူ** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**Gif ဖန်တီးသူ** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 အထက်ပါ ရုပ်ပုံကို နှိပ်၍ ဒီပရိုဂျက်နှင့် ၎င်းကို ဖန်တီးသူများအကြောင်း ဗီဒီယိုကို ကြည့်ပါ။
+> 🎥 အပေါ်ဓာတ်ပုံကို နှိပ်ပြီး သင်ခန်းစာနှင့် ဖန်တီးသူများအကြောင်း ဗီဒီယိုကြည့်ရှုပါ!
 
 ---
 
-## ပညာသင်ကြားနည်း
+## သင်ကြားမှု နည်းလမ်း
 
-ဒီသင်ရိုးကို တည်ဆောက်စဉ် အာရုံစိုက်ထားသော သင်ကြားနည်းအချက်နှစ်ချက် ရှိသည်။ ၎င်းမှာ လက်တွေ့ လေ့လာစရာ ပြီးသားဖြစ်သော project-based ပုံစံနှင့် အကြိမ်ကြိမ် ရှိသော မေးခွန်းများ (frequent quizzes) ပါဝင်ရန် ဖြစ်သည်။ ထို့အပြင် ဒီသင်ရိုးတွင် ချိတ်ဆက်မှု ရရှိစေရန် အထူးတစ်ခုသော **theme** တစ်ခု ပါဝင်သည်။
+ဒီသင်ရိုးကို တည်ဆောက်တဲ့အခါ ကျွန်တော်တို့တည့်တည့် နှစ်ချက်မှာ အဓိကထားချိတ်ဆက်ထားပါတယ် - လက်တွေ့လုပ်ဆောင်နိုင်သော **project-based** နည်းလမ်းဖြစ်ခြင်းနှင့် **အကြိမ်ကြိမ် စစ်တမ်းများပါရှိခြင်း**။ ထို့အပြင် ဒီသင်ရိုးတွင် အထူး **အကြောင်းအရာ ထူးခြားမှု** တစ်ခုပြည့်စုံထားသည်။
 
-အကြောင်းအရာများကို project တွေနှင့် သက်ဆိုင်အောင် ထားရှိခြင်းဖြင့် သင်သားများအတွက် ပိုမို စိတ်၀င်စားဖွယ်ဖြစ်စေပြီး အတွေးအမြင်များကို ပိုမို သိမ်းပိုက်နိုင်စေသည်။ သင်တန်းမတိုင်မှီ မေးခွန်းအနည်းငယ် (low-stakes quiz) တစ်ခုက သင်ယူမည့် အကြောင်းအရာကို သင်ယူသူ၏ ရည်ရွယ်ချက်ထားရှိမှုကို ဖန်တီးပေးပြီး သင်တန်းပြီးဆုံးချိန်မှာ ပြန်လည်မေးမည့် မေးခွန်းဖြင့် အတိုးအမြှင့်သိမ်းဆည်းမှုကို အတည်ပြုပေးသည်။ ဒီသင်ရိုးကို လြယ်ကူ၍ ပျော်ရွှင်စေဖို့ ဒီဇိုင်းထုတ်ထားပြီး အပိုင်းပိုင်း သို့မဟုတ် အားလုံးကိုလည်း လေ့လာနိုင်သည်။ ပရောဂျက်များက အသေးငယ်စပြီး 12 ပတ် အဆုံးသတ်ချိန်အထိ တဖြည်းဖြည်းခက်ခဲလာသည်။ ဒီသင်ရိုးတွင် ML ၏ အမှန်တကယ်သုံးပုံများအပေါ် ပို့စ်စကရစ်တစ်ခုပါဝင်ပြီး ၎င်းကို အပိုအမှတ် (extra credit) သို့မဟုတ် ဆွေးနွေးရန် အခြေခံအဖြစ် အသုံးပြုနိုင်သည်။
+အကြောင်းအရာများကို project များနှင့် ကိုက်ညီအောင် ပြုလုပ်ခြင်းက သင်ကြားသူတို့အတွက် ပိုမိုစိတ်ဝင်စားစေရန်နှင့် သိခဲ့သော အကြောင်းအရာများကို နားလည်မှု ပိုမိုမြှင့်တင်စေရန် အထောက်အကူဖြစ်စေသည်။ စာတန်းမတက်ခင် စစ်ဆေးမှုတစ်ခုပြုခြင်းသည် သင်ကြားသူ၏ သင်ယူမှုဆီကို ရည်မှန်းချက် ထားရှိပေးပြီး၊ အတန်းပြီးနောက် စစ်ဆေးမှုတစ်ခုဖြင့် ထပ်မံ သတိထားစေရန်ရှိသည်။ ဒီသင်ရိုးကို မည်သည့်အပိုင်းအဖြတ်ကိုကစ၍ပင် သန့်ရှင်းပြီး ပျော်စရာဖြစ်အောင် ဒီဇိုင်းဆွဲထားသည်။ Projects များသည် အရင်က စတင်၍ တဖြည်းဖြည်း ပြီးဆုံးချိန်တွင် ပိုမိုရှုပ်ထွေးလာသည်။ ဒီသင်ရိုးတွင် ML ရဲ့ လက်တွေ့အသုံးချမှု အတွက် နောက်ပိုင်းမှတ်ချက်တစ်ခုပါဝင်ပြီး၊ အပို အဆင့်အနေဖြင့် သို့မဟုတ် ဆွေးနွေးရန် အခြေခံအဖြစ် အသုံးပြုနိုင်သည်။
 
-> ကျွန်တော်တို့၏ [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), နှင့် [Troubleshooting](TROUBLESHOOTING.md) ညွှန်ကြားချက်များကို ရှာဖွေကြည့်ပါ။ သင့်၏ ဆောက်လုပ်တည်ဆောက်ရမည့် တုံ့ပြန်ချက်များကို ကြိုဆိုပါတယ်!
+> ကျွန်တော်တို့၏ [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), နှင့် [Troubleshooting](TROUBLESHOOTING.md) လမ်းညွှန်ချက်များကို ရှာဖွေပါ။ သင့်၏ ဆောင်းပါးပေးမှုကို ကြိုဆိုပါတယ်!
 
 ## သင်ခန်းစာတိုင်းတွင် ပါဝင်သည့်အရာများ
 
-- ရွေးချယ်၍ sketchnote
-- ရွေးချယ်၍ ထပ်ဆောင်း ဗီဒီယို
-- ဗီဒီယို လမ်းညွှန်မှု (သင်ခန်းစာအချို့တွင်သာ)
-- [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
-- ရေးသားထားသော သင်ခန်းစာ
-- project-based သင်ခန်းစာများအတွက် ပရောဂျက်ကို ဘယ်လို တည်ဆောက်ရမည်ကို အဆင့်လိုက် ညွှန်ကြားချက်များ
-- knowledge checks
-- တစ်စုံတစ်ခုသော စိန်ခေါ်မှု
-- ထပ်ဆောင်း ဖတ်စရာ
-- အပ်မဲ့တာဝန်
-- [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
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-> **ဘာသာစကားများအကြောင်း အချက်တစ်ချက်**: သင်ခန်းစာများအများစုကို Python ဖြင့် ရေးသားထားပေမယ့် R ဖြင့်လည်း ရရှိနိုင်သည့် သင်ခန်းစာများ များစွာ ရှိသည်။ R သင်ခန်းစာတစ်ခုကို ပြီးစီးရန် `/solution` ဖိုလ်ဒါကို သွားကြည့်ပြီး R သင်ခန်းစာများကို ရှာဖွေပါ။ ၎င်းတို့တွင် .rmd extension ပါပြီး ၎င်းသည် **R Markdown** ဖိုင်တစ်မျိုးဖြစ်သည်။ R Markdown ကို ရိုးရှင်းစွာ သတ်မှတ်လျှင် `code chunks` (R သို့မဟုတ် အခြားဘာသာစကားများမှ) နှင့် `YAML header` (PDF ကဲ့သို့ ထုတ်လွှင့်မှုများကို ပြသရန် ဘယ်လို format ပြုလုပ်မည်ကို ညွှန်ပြသည်) ကို `Markdown document` ထဲတွင် ထည့်သွင်းထားသည့် ဖိုင်တစ်မျိုးအဖြစ် သတ်မှတ်နိုင်သည်။ ထို့ကြောင့် ၎င်းသည် သင်၏ ကုဒ်၊ ၎င်း၏ အထွက်နှင့် သင်၏စဉ်းစားချက်များကို Markdown ဖြင့် ရေးသားနိုင်စေရန် ဖိုင်ရေးသားသူတစ်ဦးအတွက် အကောင်းဆုံး ရေးသားပုံစံတစ်ခုအဖြစ် အလုပ်လုပ်နိုင်သည်။ ထို့အပေါ် R Markdown စာရွက်များကို PDF, HTML, သို့မဟုတ် Word ကဲ့သို့သော output ဖော်မတ်များသို့ ပြန်လည်ဖော်ပြနိုင်သည်။
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-> **မေးပွဲများအကြောင်း အချက်တစ်ချက်**: မေးပွဲအားလုံးကို [Quiz App folder](../../quiz-app) အတွင်း ထည့်ထားပြီး စုစုပေါင်း မေးခွန်း 52 ခုရှိပြီး မေးခွန်းသုံးခု နှင့် တစ်ခုချင်းစီပါဝင်သည်။ မေးခွန်းများကို သင်ခန်းစာများထဲတွင် ချိတ်ဆက်ထားပေမယ့် quiz app ကို ဒေသဖြင့်လည်း ပြေးနိုင်ပါသည်; ဒေသခံ host လုပ်ရန် သို့မဟုတ် Azure တွင် deploy ပြုလုပ်ရန် `quiz-app` ဖိုလ်ဒါ၌ ပါသော ညွှန်ကြားချက်ကို လိုက်နာပါ။
-
-| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                စက်သင်ယူမှုအတွက် နိဒါန်း                |      [Introduction](1-Introduction/README.md)       | စက်သင်ယူမှု၏ အခြေခံ အယူအဆများကို လေ့လာပါ                                                                               |                                             [သင်ခန်းစာ](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                စက်သင်ယူမှု၏ သမိုင်း                |      [Introduction](1-Introduction/README.md)       | ဤနယ်ပယ်၏ နောက်ခံ သမိုင်းကို သင်ယူပါ                                                                                        |                                            [သင်ခန်းစာ](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 တရားမျှတမှုနှင့် စက်သင်ယူမှု                  |      [Introduction](1-Introduction/README.md)       | ML မော်ဒယ်များကို တည်ဆောက်ပြီး အသုံးချရာတွင် ကျောင်းသားများ စဉ်းစားသင့်သော တရားမျှတမှုဆိုင်ရာ အရေးကြီးသော အချက်များက ဘာတွေရှိပါသလဲ။ |                                              [သင်ခန်းစာ](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                စက်သင်ယူမှု နည်းဗျူဟာများ                 |      [Introduction](1-Introduction/README.md)       | ML သုတေသနပညာရှင်များသည် ML မော်ဒယ်များ ဖန်တီးရာတွင် မည်သည့် နည်းလမ်းများကို အသုံးပြုကြသနည်း။                                                                       |                                          [သင်ခန်းစာ](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Regression အတွက် နိဒါန်း                   |        [Regression](2-Regression/README.md)         | Regression မော်ဒယ်များအတွက် Python နှင့် Scikit-learn ဖြင့် အစပြုပါ                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                မြောက်အမေရိက မုန်လာဥဈေးများ 🎃                |        [Regression](2-Regression/README.md)         | ML အတွက် ပြင်ဆင်ရာတွင် ဒေတာကို ကြည့်ရှု၍ သန့်စင်ပါ                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                မြောက်အမေရိက မုန်လာဥဈေးများ 🎃                |        [Regression](2-Regression/README.md)         | လိုင်းနီယာ (linear) နှင့် ပိုလစ်နိုမီယယ် (polynomial) regression မော်ဒယ်များ ဖန်တီးပါ                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                မြောက်အမေရိက မုန်လာဥဈေးများ 🎃                |        [Regression](2-Regression/README.md)         | Logistic regression မော်ဒယ် တစ်ခု တည်ဆောက်ပါ                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          ဝက်ဘ် အက်ပ် 🔌                          |           [Web App](3-Web-App/README.md)            | လေ့ကျင့်ပြီးသော မော်ဒယ်ကို အသုံးပြုရန် ဝက်ဘ် အက်ပ်တစ်ခု တည်ဆောက်ပါ                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Classification အတွက် နိဒါန်း                 |    [Classification](4-Classification/README.md)     | ဒေတာကို သန့်စင်၊ ပြင်ဆင်၊ ကြည့်ရှုခြင်းနှင့် classification မိတ်ဆက်                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             အာရှနှင့် အိန္ဒိယ ထမင်းအရသာများ 🍜             |    [Classification](4-Classification/README.md)     | Classifier များ မိတ်ဆက်                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
-|      12       |             အာရှနှင့် အိန္ဒိယ ထမင်းအရသာများ 🍜             |    [Classification](4-Classification/README.md)     | Classifier များပို၍                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
-|      13       |             အာရှနှင့် အိန္ဒိယ ထမင်းအရသာများ 🍜             |    [Classification](4-Classification/README.md)     | မော်ဒယ်ကို အသုံးပြု၍ အကြံပြုဝက်ဘ်အက်ပ်တစ်ခု တည်ဆောက်ပါ                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Clustering အတွက် နိဒါန်း                   |        [Clustering](5-Clustering/README.md)         | ဒေတာကို သန့်စင်၊ ပြင်ဆင်၊ ကြည့်ရှုခြင်းနှင့် clustering မိတ်ဆက်                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              နိုက်ဂျီးရီးယား ဂီတအရသာ စူးစမ်းခြင်း 🎧              |        [Clustering](5-Clustering/README.md)         | K-Means clustering နည်းပညာကို စူးစမ်းရှာဖွေပါ                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        သဘာဝဘာသာစကား ကိုင်တွယ်ခြင်း (NLP) အတွက် နိဒါန်း ☕️         |   [Natural language processing](6-NLP/README.md)    | ရိုးရှင်းသော ဘော့တစ်ခု တည်ဆောက်ကာ NLP အခြေခံများကို လေ့လာပါ                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      NLP အတွက် ပုံမှန်လုပ်ဆောင်မှုများ ☕️                      |   [Natural language processing](6-NLP/README.md)    | ဘာသာစကား ဖွဲ့စည်းမှုများကို ကိုင်တွယ်ရာတွင် လိုအပ်သော ပုံမှန် လုပ်ဆောင်မှုများကို နားလည်ခြင်းဖြင့် သင်၏ NLP အသိကို နက်ရှိုင်းစေပါ                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             ဘာသာပြန်ခြင်းနှင့် စိတ်ခံစားချက် သုံးသပ်ခြင်း ♥️              |   [Natural language processing](6-NLP/README.md)    | Jane Austen အလုပ်များနှင့် အတူ ဘာသာပြန်ခြင်းနှင့် စိတ်ခံစားချက် သုံးသပ်ခြင်း                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  ဥရောပရဲ့ ချစ်မှုနှင့်သက်ဆိုင်သည့် ဟိုတယ်များ ♥️                  |   [Natural language processing](6-NLP/README.md)    | ဟိုတယ် သုံးသပ်ချက်များဖြင့် စိတ်ခံစားချက် သုံးသပ်ခြင်း 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  ဥရောပရဲ့ ချစ်မှုနှင့်သက်ဆိုင်သည့် ဟိုတယ်များ ♥️                  |   [Natural language processing](6-NLP/README.md)    | ဟိုတယ် သုံးသပ်ချက်များဖြင့် စိတ်ခံစားချက် သုံးသပ်ခြင်း 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            အချိန်စီးရီး မျှော်မှန်းခြင်း အတွက် နိဒါန်း             |        [Time series](7-TimeSeries/README.md)        | အချိန်စီးရီး မျှော်မှန်းခြင်း အတွက် နိဒါန်း                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ ကမ္ဘာလုံးဆိုင်ရာ စွမ်းအင် အသုံးပြုမှု ⚡️ - ARIMA ဖြင့် အချိန်စီးရီး မျှော်မှန်းခြင်း |        [Time series](7-TimeSeries/README.md)        | ARIMA ဖြင့် အချိန်စီးရီး မျှော်မှန်းခြင်း                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ ကမ္ဘာလုံးဆိုင်ရာ စွမ်းအင် အသုံးပြုမှု ⚡️ - SVR ဖြင့် အချိန်စီးရီး မျှော်မှန်းခြင်း  |        [Time series](7-TimeSeries/README.md)        | Support Vector Regressor (SVR) ဖြင့် အချိန်စီးရီး မျှော်မှန်းခြင်း                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Reinforcement learning အတွက် နိဒါန်း             | [Reinforcement learning](8-Reinforcement/README.md) | Q-Learning ဖြင့် Reinforcement learning မိတ်ဆက်                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Peter ကို ခြင်္သေ့မှ ရှောင်ရှားနိုင်하도록 ကူညီပါ! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            အမှန်တကယ်ရှိသော ML အခြေအနေများနှင့် အသုံးချမှုများ            |      [ML in the Wild](9-Real-World/README.md)       | ရိုးရာ ML ကို အသုံးချသော စိတ်ဝင်စားစရာနှင့် သက်သေပြသော အမှန်ကျ အသုံးချမှုများ                                                               |                                             [သင်ခန်းစာ](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            RAI dashboard ကို အသုံးပြုပ၍ ML မော်ဒယ်များ ပြင်ဆင် စစ်ဆေးခြင်း          |      [ML in the Wild](9-Real-World/README.md)       | Responsible AI dashboard အစိတ်အပိုင်းများကို အသုံးပြု၍ စက်သင်ယူမှု မော်ဒယ်များကို စစ်ဆေးပြီး ပြင်ဆင်ခြင်း                                                              |                                             [သင်ခန်းစာ](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [ဤသင်တန်းနှင့်သက်ဆိုင်သော ထပ်ဆင့် အရင်းအမြစ်များအားလုံးကို ကျွန်ုပ်တို့၏ Microsoft Learn စုစည်းမှုတွင် ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-## အော့ဖ်လိုင်း ဝင်ရောက်မှု
-
-ဤစာတမ်းကို [Docsify](https://docsify.js.org/#/) ကို အသုံးပြု၍ အော့ဖ်လိုင်းတွင် လည်ပတ်နိုင်သည်။ ဤ repo ကို fork လုပ်ပြီး၊ သင်၏ ဒေသတွင်း စက်ပေါ်တွင် [Docsify](https://docsify.js.org/#/quickstart) ကို တပ်ဆင်ပါ၊ ထို့နောက် ဤ repo ၏ ရှေ့ခြေ ဖိုလ်ဒါထဲတွင် `docsify serve` ဟု ရိုက်ထည့်ပါ။ ဝက်ဘ်ဆိုက်ကို သင်၏ localhost ပေါ်တွင် port 3000 တွင် ယူဆောင်ပြသမည်ဖြစ်သည်: `localhost:3000`.
-
-## PDF များ
-
-သင်ရိုးညွှန်းစာရင်း၏ PDF ကို link များနှင့်အတူ [ဤနေရာတွင်](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) ရှာနိုင်ပါသည်။
-
+- ရွေးချယ်နိုင်သည့် စကက်ချ်မှတ်စု
+- ရွေးချယ်နိုင်သည့် ပံ့ပိုးဗီဒီယို
+- ဗီဒီယို လမ်းပြချက် (သင်ခန်းစာအချို့သာ)
+- [အတန်းမတက်ခင် အပူအဆွေး စစ်တမ်းစစ်ဆေးမှု](https://ff-quizzes.netlify.app/en/ml/)
+- ရေးသားထားသည့် သင်ခန်းစာ
+- project-based သင်ခန်းစာများအတွက် project တည်ဆောက်ရမည့်အဆင့်ခြား ညွှန်ကြားချက်များ
+- သိမှုစစ်ဆေးမှုများ
+- စိန်ခေါ်မှုတစ်ခု
+- ပံ့ပိုးဖတ်ရှုစာအုပ်များ
+- လေ့ကျင့်ခန်း
+- [အတန်းပြီးနောက် စစ်တမ်းစစ်ဆေးမှု](https://ff-quizzes.netlify.app/en/ml/)
+
+> **ဘာသာစကားအကြောင်း မှတ်ချက်** ။ ဒီသင်ခန်းစာများကို အဓိက Python ဖြင့်ရေးသားထားပေမယ့် R ဘာသာစကားဖြင့်လည်း ရရှိနိုင်သည်။ R သင်ခန်းစာတစ်ခု ပြီးစေရန် `/solution` ဖိုလ်ဒါထဲ R သင်ခန်းစာများရှာဖွေပါ။ အဲဒီမှာရှိတဲ့ .rmd extension ဖြစ်တဲ့ ဖိုင်တွေက **R Markdown** ဖိုင်မျိုးဖြစ်ပြီး `code chunks` (R သို့မဟုတ် အခြားဘာသာစကားများ) နဲ့ `YAML header` (PDF နဲ့ output များကို ဖော်ညွှန်းရေးဆွဲရာတွင် အသုံးပြုသည်) ကို Markdown ဆိုင်ရာစာတမ်းထဲ ညွှန်ဟပ်ထားခြင်းဖြစ်သည်။ ဒီနည်းဖြင့် သင့် code၊ အသုံးပြုမှုအဖြစ်၊ နှင့် စိတ်ကူးများကို Markdown ဖြင့် တိုက်ရိုက် ရေးသားနိုင်ပြီး၊ R Markdown စာတမ်းများကို PDF, HTML, သို့မဟုတ် Word output format မျိုးသို့ ပြန်လည်ဖော်ပြနိုင်သည်။
+> **ကွစ်များအကြောင်း မှတ်ချက်**: ကွစ်အားလုံးကို [Quiz App ဖိုလ်ဒါ](../../quiz-app) တွင်စုစည်းထားပြီး၊ တစ်ခုချင်းစီတွင်မေးခွန်း သုံးခုပါဝင်သည့် စုစုပေါင်း ၅၂ ခုရှိသည်။ မိမိသင်ခန်းစာများမှ ချိတ်ဆက်ထားသော်လည်း၊ quiz app ကို ဒေသစုံတွင် ပြေးနိုင်ပြီး၊ ဒေသစုံတွင် ဟိုစ်တင်ခြင်း သို့မဟုတ် Azure တွင် တင်ရန် `quiz-app` ဖိုလ်ဒါအတွင်း လမ်းညွှန်ချက်အား လိုက်နာပါ။
+
+| သင်ခန်းစာနံပါတ် |                                ခေါင်းစဉ်                                |                    သင်ခန်းစာအုပ်စု                    | သင်ယူရမည့် ရည်ရွယ်ချက်များ                                                                                                 |                                                               ချိတ်ဆက်ထားသည့် သင်ခန်းစာ                                                               |                      ရေးသားသူ                       |
+| :--------------: | :---------------------------------------------------------------------: | :----------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------: |
+|        ၀၁        |                      စက်မှုသင်ယူမှုအတွက် အနိမ့်အကျဉ်း                     |           [Introduction](1-Introduction/README.md)           | စက်မှုသင်ယူမှုနောက်ခံ အခြေခံအယူအဆများကို သင်ယူရန်                                                                   |                                           [Lesson](1-Introduction/1-intro-to-ML/README.md)                                           |                      Muhammad                       |
+|        ၀၂        |                        စက်မှုသင်ယူမှု၏ သမိုင်းကြောင်း                         |           [Introduction](1-Introduction/README.md)           | ဒီကွင်းဆက် အခြေခံ သမိုင်းကို သင်ယူရန်                                                                                  |                                          [Lesson](1-Introduction/2-history-of-ML/README.md)                                          |                    Jen and Amy                      |
+|        ၀၃        |                        တရားမျှတမှုနှင့် စက်မှုသင်ယူမှု                         |           [Introduction](1-Introduction/README.md)           | ML မော်ဒယ်များ တည်ဆောက်ခြင်းနှင့် အသုံးချခြင်းအခါတွင် ကျောင်းသားများ  တွေးထားသင့်သည့် တရားမျှတမှုဆိုင်ရာ ဒဿနိက အရေးပါချက်များက ဘာတွေလဲ? |                                             [Lesson](1-Introduction/3-fairness/README.md)                                              |                      Tomomi                         |
+|        ၀၄        |                        စက်မှုသင်ယူမှု နည်းပညာများ                            |           [Introduction](1-Introduction/README.md)           | ML သုတေသနပညာရှင်များ သုံးသော နည်းပညာများ ဘာတွေ ဖြစ်မလဲ?                                                              |                                        [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                         |                   Chris and Jen                     |
+|        ၀၅        |                          Regression အတွက် နိဒါန်း                           |             [Regression](2-Regression/README.md)             | Python နှင့် Scikit-learn ဖြင့် regression မော်ဒယ်များ စတင်ရန်                                                               |            [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)             |          Jen • Eric Wanjau           |
+|        ၀၆        |                     မြောက်အမေရိက ခရ Autumnဖရံ၏ စျေးနှုန်း 🎃                |             [Regression](2-Regression/README.md)             | ML အတွက် ဒေတာများ ဖော်ပြ ကြည့်ခြင်းနှင့် သန့်ရှင်း လုပ်ဆောင်ရန်                                                              |             [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)              |          Jen • Eric Wanjau           |
+|        ၀၇        |                     မြောက်အမေရိက ခရ Autumnဖရံ၏ စျေးနှုန်း 🎃                |             [Regression](2-Regression/README.md)             | လိုင်းနဲ့ ပေါ်လီနောမြယ် regression မော်ဒယ်များ တည်ဆောက်ရန်                                                                |           [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)            |          Jen and Dmitry • Eric Wanjau           |
+|        ၀၈        |                     မြောက်အမေရိက ခရ Autumnဖရံ၏ စျေးနှုန်း 🎃                |             [Regression](2-Regression/README.md)             | Logistic regression မော်ဒယ် တစ်ခု တည်ဆောက်ရန်                                                                             |       [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)        |          Jen • Eric Wanjau           |
+|        ၀၉        |                              ဝက်ဘ်အက်ပ် 🔌                               |              [Web App](3-Web-App/README.md)                 | သင်၏ ပညာသင်ဆယ်ထားသော မော်ဒယ်ကို အသုံးပြုရန် ဝက်ဘ်အက်ပ် တည်ဆောက်ရန်                                                     |                                              [Python](3-Web-App/1-Web-App/README.md)                                               |                         Jen                          |
+|        ၁၀        |                     classification အတွက် နိဒါန်း                         |          [Classification](4-Classification/README.md)         | သင့်ဒေတာကို သန့်ရှင်း ပြင်ဆင်ပြီး ဖော်ပြခြင်းနှင့် classification အတွက် နိဒါန်း                                         | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen and Cassie • Eric Wanjau |
+|        ၁၁        |             မိဿာန်းအာရှနှင့် အိန္ဒိယ ဟင်းလွန်များ 🍜                   |          [Classification](4-Classification/README.md)         | Classifier များအကြောင်း နိဒါန်း                                                                                           | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|        ၁၂        |             မိဿာန်းအာရှနှင့် အိန္ဒိယ ဟင်းလွန်များ 🍜                   |          [Classification](4-Classification/README.md)         | Classifier များ ပိုမိုသိရှိရန်                                                                                             | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
+|        ၁၃        |             မိဿာန်းအာရှနှင့် အိန္ဒိယ ဟင်းလွန်များ 🍜                   |          [Classification](4-Classification/README.md)         | မော်ဒယ်ကို အသုံးပြုကာ recommender ဝက်ဘ်အက်ပ် တည်ဆောက်ရန်                                                                |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|        ၁၄        |                         Clustering အတွက် နိဒါန်း                           |             [Clustering](5-Clustering/README.md)             | သင့်ဒေတာကို သန့်ရှင်း ပြင်ဆင်ပြီး ဖော်ပြခြင်းနှင့် clustering အတွက် နိဒါန်း                                               |           [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)           |          Jen • Eric Wanjau           |
+|        ၁၅        |                  နိုင်ဂျီးရီးယား တေးဂီတ စားသောက်ဖက်စပ်ခြင်း 🎧            |             [Clustering](5-Clustering/README.md)             | K-Means clustering နည်းကို လေ့လာသုံးသပ်ရန်                                                                                 |             [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)            |          Jen • Eric Wanjau           |
+|        ၁၆        |                   ဘာသာစကားသဘာဝ ကိုင်တွယ်ခြင်း နိဒါန်း ☕️             |         [Natural language processing](6-NLP/README.md)        | ရိုးရှင်းသော bot တစ်ခု ဖန်တီးခြင်းဖြင့် NLP အခြေခံကို သင်ယူရန်                                                        |                                           [Python](6-NLP/1-Introduction-to-NLP/README.md)                                           |                      Stephen                        |
+|        ၁၇        |                    NLP ရဲ့ ပုံမှန်တာဝန်များ ☕️                       |         [Natural language processing](6-NLP/README.md)        | ဘာသာစကားဖွဲ့စည်းမှုများနှင့် ဆက်စပ်၍ လုပ်ဆောင်ရမည့် ပုံမှန်တာဝန်များကို နက်ရှိုင်းစွာ လေ့လာရန်                           |                                                       [Python](6-NLP/2-Tasks/README.md)                                                    |                      Stephen                        |
+|        ၁၈        |                  ဘာသာပြန်ခြင်းနှင့် စိတ်ခံစားချက် ခွဲခြမ်းစိတ်ဖြာခြင်း ♥️     |         [Natural language processing](6-NLP/README.md)        | Jane Austen နှင့် အတူ ဘာသာပြန်ခြင်းနှင့် စိတ်ခံစားချက် ခွဲခြမ်းစိတ်ဖြာခြင်း                                                |                                          [Python](6-NLP/3-Translation-Sentiment/README.md)                                          |                      Stephen                        |
+|        ၁၉        |                      ဥရောပ၏ ရင်ခုန်စရာ ဟိုတယ်များ ♥️                      |         [Natural language processing](6-NLP/README.md)        | ဟိုတယ်ပြန်ကြားချက် အသုံးပြု၍ စိတ်ခံစားချက် ခွဲခြမ်းစိတ်ဖြာခြင်း ၁                                                |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                               |                      Stephen                        |
+|        ၂၀        |                      ဥရောပ၏ ရင်ခုန်စရာ ဟိုတယ်များ ♥️                      |         [Natural language processing](6-NLP/README.md)        | ဟိုတယ်ပြန်ကြားချက် အသုံးပြု၍ စိတ်ခံစားချက် ခွဲခြမ်းစိတ်ဖြာခြင်း ၂                                                |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                               |                      Stephen                        |
+|        ၂၁        |                    အချိန်စီးရီး ခန့်မှန်းချက် အတွက် နိဒါန်း                 |             [Time series](7-TimeSeries/README.md)             | အချိန်စီးရီး ခန့်မှန်းချက် အတွက် နိဒါန်း                                                                             |                                           [Python](7-TimeSeries/1-Introduction/README.md)                                           |                    Francesca                       |
+|        ၂၂        | ⚡️ ကမ္ဘာ့ လျှပ်စစ် စွန့်စားမှု ⚡️ - ARIMA ဖြင့် အချိန်စီးရီး ခန့်မှန်းခြင်း  |             [Time series](7-TimeSeries/README.md)             | ARIMA နည်းဖြင့် အချိန်စီးရီး ခန့်မှန်းခြင်း                                                                              |                                              [Python](7-TimeSeries/2-ARIMA/README.md)                                              |                    Francesca                       |
+|        ၂၃        |  ⚡️ ကမ္ဘာ့ လျှပ်စစ် စွန့်စားမှု ⚡️ - SVR ဖြင့် အချိန်စီးရီး ခန့်မှန်းခြင်း   |             [Time series](7-TimeSeries/README.md)             | Support Vector Regressor နည်းဖြင့် အချိန်စီးရီး ခန့်မှန်းခြင်း                                                        |                                               [Python](7-TimeSeries/3-SVR/README.md)                                               |                     Anirban                        |
+|        ၂၄        |                   အားပေးသင်ယူမှု နိဒါန်း                              |     [Reinforcement learning](8-Reinforcement/README.md)      | Q-Learning ဖြင့် အားပေးသင်ယူမှု အတွက် နိဒါန်း                                                                        |                                           [Python](8-Reinforcement/1-QLearning/README.md)                                           |                      Dmitry                         |
+|        ၂၅        |                   Peter ကို အမောကြမ်းတို့မှ ကာကွယ်ရန်! 🐺               |     [Reinforcement learning](8-Reinforcement/README.md)      | Reinforcement learning Gym များ                                                                                         |                                             [Python](8-Reinforcement/2-Gym/README.md)                                              |                      Dmitry                         |
+|    နောက်ထပ် မှတ်စု   |                 အမှန်တကယ် အလုပ်လုပ်နေသော ML ရှုမြင်ကွင်းများနှင့် လျှောက်လွှာများ                |             [ML in the Wild](9-Real-World/README.md)             | ပုံမှန် ML ၏ စိတ်ဝင်စားဖွယ် နှင့် ဖော်ပြချက်များ                                                                         |                                           [Lesson](9-Real-World/1-Applications/README.md)                                            |                         Team                         |
+|    နောက်ထပ် မှတ်စု   |                  RAI dashboard ဖြင့် ML တွင် မော်ဒယ်ပြင်ဆင်ခြင်း               |             [ML in the Wild](9-Real-World/README.md)             | Responsible AI dashboard အစိတ်အပိုင်းများဖြင့် စက်မှုသင်ယူမော်ဒယ်များ ပြင်ဆင်ခြင်း                                                 |                                           [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                      Ruth Yakubu                     |
+
+> [ဒီသင်တန်းအတွက် အပိုဆောင်း အရင်းအမြစ်များအားလုံးကို Microsoft Learn စုစည်းမှုတွင် ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
+## အော့ဖ်လိုင်း ဝင်ရောက်ကြည့်ရှုနိုင်မှု
+
+[Docsify](https://docsify.js.org/#/) သုံးပြီး ဒီစာတမ်းကို အော့ဖ်လိုင်းတွင် ပြေးဆွဲနိုင်သည်။ ဒီ repo ကို fork လုပ်ပြီး၊ သင့်ဒေသစုံကွန်ပျူတာတွင် [Docsify ကို ထည့်သွင်းပါ](https://docsify.js.org/#/quickstart)၊ ထို့နောက် repo ရဲ့ အခြေခံ ဖိုလ်ဒါတွင် `docsify serve` ဟု ရိုက်ထည့်ပါ။ ဝက်ဘ်ဆိုက်ကို သင့် localhost: 3000 ပေါက်(Port)တွင် ဝင်ရောက်ကြည့်ရှုနိုင်ပါမည်။
+
+## PDFs
+
+သင်ခန်းစာအစီအစဉ် PDF ကို [ဒီနေရာတွင်](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) ရှာဖွေပါ။
 
 ## 🎒 အခြား သင်တန်းများ
 
-ကျွန်ုပ်တို့၏ အဖွဲ့သည် အခြား သင်တန်းများလည်း ထုတ်လုပ်ပါသည်! ကြည့်ရှုပါ:
+ကျွန်ုပ်တို့အဖွဲ့သည် အခြားသင်တန်းများကိုလည်း ထုတ်လုပ်လျက်ရှိသည်! စစ်ဆေးကြည့်ပါ:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -172,51 +180,51 @@ Cloud Advocates တွေက Microsoft မှ အသင်းအဖွဲ့သ
 ---
 
 ### Azure / Edge / MCP / Agents
-[![AZD အစပြုသူများအတွက်](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Edge AI အစပြုသူများအတွက်](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![MCP အစပြုသူများအတွက်](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents အစပြုသူများအတွက်](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Generative AI Series
-[![Generative AI အစပြုသူများအတွက်](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![ဖန်တီးနိုင်သော AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![ဖန်တီးနိုင်သော AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### အဓိက သင်ယူမှု
-[![စတင်လေ့လာသူများအတွက် ML](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![စတင်လေ့လာသူများအတွက် ဒေတာ သိပ္ပံ](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![စတင်လေ့လာသူများအတွက် AI](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![စတင်လေ့လာသူများအတွက် ဆိုက်ဘာလုံခြုံရေး](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![စတင်လေ့လာသူများအတွက် ဝက်ဘ် ဖွံ့ဖြိုးရေး](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![စတင်လေ့လာသူများအတွက် IoT](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![စတင်လေ့လာသူများအတွက် XR ဖွံ့ဖြိုးရေး](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+### အခြေခံသင်ယူမှု
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Copilot စီးရီး
-[![AI တွဲဖက် ပရိုဂရမ်ရေးရန် Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![C#/.NET အတွက် Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Copilot စွန့်စားခန်း](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+### Copilot စီးရီးများ
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## အကူအညီ ရယူခြင်း
+## ကူညီမှုရယူခြင်း
 
-AI အက်ပ်များ ဖန်တီးရာတွင် အခက်အခဲ ဖြစ်လာပါက သို့မဟုတ် မေးခွန်းများ ရှိပါက MCP အကြောင်းကို သင်ယူနေသူများနှင့် အတွေ့အကြုံရှိ ဖွံ့ဖြိုးသူများနှင့် ပူးပေါင်းပြီး ဆွေးနွေးနိုင်သည်။ ဤသည်မှာ မေးခွန်းများကို ကြိုဆိုပြီး သတင်းအချက်အလက်များကို လွတ်လပ်စွာ မျှဝေကြသော ပံ့ပိုးမှုရှိသော လူမှုအသိုင်းအဝိုင်းတစ်ခုဖြစ်သည်။
+AI appများ တည်ဆောက်ရာတွင် အခက်အခဲတွင် ရင်ဆိုင်ရပါက သို့မဟုတ် မေးခွန်းများ ရှိပါက MCP ကို ပတ်သက်၍ သင်ယူသူများနှင့် အတွေ့အကြုံရှိသော developer များနှင့် ဆွေးနွေးပွဲများတွင် ပါဝင်ဆက်သွယ်နိုင်ပါသည်။ မေးခွန်းများကို ဝမ်းမြောက်စွာ လက်ခံပေးသည့် နှင့် အသိပညာကို အခြားသူများနှင့် အခမဲ့ဝေမျှပေးသည့် ပံ့ပိုးကူညီမှုအသိုင်းအဝိုင်းတစ်ခုဖြစ်ပါသည်။
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ထုတ်ကုန် အတွက် တုံ့ပြန်ချက်များ သို့မဟုတ် တည်ဆောက်စဉ် အမှားများ ရှိပါက ဤနေရာသို့ သွားပါ။
+ထုတ်ကုန်အပေါ် တုံ့ပြန်ချက်များ သို့မဟုတ် တည်ဆောက်စဉ် အမှားများ ရှိပါက ဖော်ပြပါနေရာကို သွားရောက်ကြည့်ရှုနိုင်ပါသည် - 
 
-[![Microsoft Foundry ဖွံ့ဖြိုးသူ ဖိုရမ်](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-သတိပေးချက်:
-ဤစာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု Co-op Translator (https://github.com/Azure/co-op-translator) ဖြင့် ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှုအတွက် ကြိုးစားပေမယ့် အလိုအလျောက် ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်ချက်များ ပါဝင်နိုင်ကြောင်း သတိပြုပါ။ မူလစာတမ်းကို မူလဘာသာဖြင့်သာ တရားဝင် အရင်းအမြစ်အဖြစ် ယူဆသင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် ပရော်ဖက်ရှင်နယ် လူဘာသာပြန်ထံမှ ဘာသာပြန်ချက်ကို အတည်ပြုရန် အကြံပြုပါသည်။ ဤဘာသာပြန်ချက်ကို အသုံးပြုမှုကြောင့် ဖြစ်ပေါ်လာသော နားလည်မှုပျက်ခြင်းများ သို့မဟုတ် အဓိပ္ပာယ်လွဲမှားခြင်းများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။
+**အတည်မပြုချက်**  
+ဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) မှတဆင့် ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုကို ကြိုးစားကြပေမယ့်၊ စက်ရုပ်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် တိကျမှုလျော့ပါးမှုများ ပါဝင်နိုင်ကြောင်း သတိပြုပါရန် အလေးထားပါသည်။ မူလစာရွက်စာတမ်းသည် သူ၏မူရင်းဘာသာဖြင့်သာ ထိထိရောက်ရောက် သိထားရမည့်အချက်အလက် အရင်းအမြစ်ဖြစ်ပါသည်။ အရေးပါသောအချက်အလက်များအတွက် ပရော်ဖက်ရှင်နယ် လူသားဘာသာပြန်ဝန်ဆောင်မှုကို အသုံးပြုရန် အကြံပြုပါသည်။ ဤဘာသာပြန်မှုအသုံးပြုပြီး ဖြစ်ပေါ်လာနိုင်သည့် မျှော်မှန်းမှုမှားယွင်းမှုများ သို့မဟုတ် နားမလည်မှုများအတွက် ကျွန်ုပ်တို့ တာဝန်မယူပါ။
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/uk/README.md b/translations/uk/README.md
index 63bdd6cb5..1d4439aea 100644
--- a/translations/uk/README.md
+++ b/translations/uk/README.md
@@ -1,164 +1,173 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T13:59:33+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T16:44:18+00:00",
   "source_file": "README.md",
   "language_code": "uk"
 }
 -->
-[![Ліцензія GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![Співавтори GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![Проблеми GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![Pull-запити GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs Ласкаво просимо](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![Спостерігачі GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![Форки GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![Зірки GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
-### 🌐 Підтримка кількох мов
+### 🌐 Підтримка багатьох мов
 
-#### Підтримується через GitHub Action (Автоматизовано та завжди актуально)
+#### Підтримується через GitHub Action (автоматично та завжди оновлено)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Арабська](../ar/README.md) | [Бенгальська](../bn/README.md) | [Болгарська](../bg/README.md) | [Бірманська (М'янма)](../my/README.md) | [Китайська (спрощена)](../zh/README.md) | [Китайська (традиційна, Гонконг)](../hk/README.md) | [Китайська (традиційна, Макао)](../mo/README.md) | [Китайська (традиційна, Тайвань)](../tw/README.md) | [Хорватська](../hr/README.md) | [Чеська](../cs/README.md) | [Данська](../da/README.md) | [Нідерландська](../nl/README.md) | [Естонська](../et/README.md) | [Фінська](../fi/README.md) | [Французька](../fr/README.md) | [Німецька](../de/README.md) | [Грецька](../el/README.md) | [Іврит](../he/README.md) | [Хінді](../hi/README.md) | [Угорська](../hu/README.md) | [Індонезійська](../id/README.md) | [Італійська](../it/README.md) | [Японська](../ja/README.md) | [Каннада](../kn/README.md) | [Корейська](../ko/README.md) | [Литовська](../lt/README.md) | [Малайська](../ms/README.md) | [Малаялам](../ml/README.md) | [Маратхі](../mr/README.md) | [Непальська](../ne/README.md) | [Нігерійський піджин](../pcm/README.md) | [Норвезька](../no/README.md) | [Перська (фарсі)](../fa/README.md) | [Польська](../pl/README.md) | [Португальська (Бразилія)](../br/README.md) | [Португальська (Португалія)](../pt/README.md) | [Панджабі (гурмукхі)](../pa/README.md) | [Румунська](../ro/README.md) | [Російська](../ru/README.md) | [Сербська (кирилична)](../sr/README.md) | [Словацька](../sk/README.md) | [Словенська](../sl/README.md) | [Іспанська](../es/README.md) | [Свахілі](../sw/README.md) | [Шведська](../sv/README.md) | [Тагальська (філіппінська)](../tl/README.md) | [Тамільська](../ta/README.md) | [Телугу](../te/README.md) | [Тайська](../th/README.md) | [Турецька](../tr/README.md) | [Українська](./README.md) | [Урду](../ur/README.md) | [В'єтнамська](../vi/README.md)
+[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](./README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
+
+> **Віддаєте перевагу клонувати локально?**
+
+> У цьому репозиторії є понад 50 мовних перекладів, що суттєво збільшує розмір завантаження. Щоб клонувати без перекладів, використовуйте sparse checkout:
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> Це дасть вам усе необхідне для проходження курсу з набагато швидшим завантаженням.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### Приєднуйтесь до нашої спільноти
 
-[![Discord Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-У нас триває серія заходів у Discord під назвою "Навчайся зі ШІ", дізнайтеся більше та приєднуйтесь до нас на [Серія «Навчання зі ШІ»](https://aka.ms/learnwithai/discord) з 18 по 30 вересня 2025 року. Ви отримаєте поради та прийоми використання GitHub Copilot для Data Science.
+Ми проводимо серію Discord "Вчимося з AI", дізнайтеся більше і приєднуйтесь до нас на [Learn with AI Series](https://aka.ms/learnwithai/discord) з 18 по 30 вересня 2025 року. Ви отримаєте поради та трюки з використання GitHub Copilot для Data Science.
 
-![Серія «Навчання зі ШІ»](../../translated_images/uk/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/uk/3.9b58fd8d6c373c20.webp)
 
-# Машинне навчання для початківців - навчальна програма
+# Машинне навчання для початківців — навчальна програма
 
-> 🌍 Подорожуйте навколо світу, досліджуючи машинне навчання через призму світових культур 🌍
+> 🌍 Подорожуйте світом, досліджуючи машинне навчання через культури різних країн 🌍
 
-Cloud Advocates у Microsoft раді запропонувати 12-тижневу, 26-урочну навчальну програму, присвячену темі **Машинного навчання**. У цій програмі ви дізнаєтесь про те, що іноді називають **класичним машинним навчанням**, використовуючи переважно бібліотеку Scikit-learn і уникаючи глибокого навчання, яке висвітлено в нашому курсі [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Поєднуйте ці уроки з нашим курсом ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners).
+Cloud Advocates у Microsoft раді запропонувати 12-тижневу програму з 26 уроків, присвячену **Машинному навчанню**. У цій навчальній програмі ви дізнаєтесь про те, що інколи називають **класичним машинним навчанням**, використовуючи передусім бібліотеку Scikit-learn та уникаючи глибинного навчання, яке висвітлюється в нашій [навчальній програмі AI для початківців](https://aka.ms/ai4beginners). Поєднуйте ці уроки з нашою ['Data Science for Beginners' програмою](https://aka.ms/ds4beginners)!
 
-Подорожуйте з нами навколо світу, застосовуючи ці класичні техніки до даних із різних регіонів. Кожен урок включає опитування до та після уроку, письмові інструкції для виконання уроку, рішення, завдання та інше. Наша проектно-орієнтована педагогіка дозволяє вчитися шляхом створення — перевірений спосіб для надійного засвоєння нових навичок.
+Подорожуйте з нами навколо світу, застосовуючи класичні методи до даних із різних регіонів світу. Кожен урок включає тести до та після заняття, письмові інструкції для виконання уроку, рішення, завдання тощо. Наша проектно-орієнтована методика дозволяє вчитися, створюючи власні проекти — перевірений спосіб надовго засвоїти нові навички.
 
-**✍️ Величезна подяка нашим авторам** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ Щирі подяки авторам** Джен Лупер, Стівен Гауелл, Франческа Лаззері, Томомі Імура, Кассі Бревіу, Дмитру Сошникову, Крісу Норінгу, Анірбану Мукхерджі, Орнеллі Альтунян, Рут Якубу та Емі Бойд
 
-**🎨 Також дякуємо нашим ілюстраторам** Tomomi Imura, Dasani Madipalli та Jen Looper
+**🎨 Подяки ілюстраторам** Томомі Імура, Дасані Мадіпаллі та Джен Лупер
 
-**🙏 Особлива подяка 🙏 нашим студентським амбасадорам Microsoft — авторам, рецензентам та учасникам контенту**, зокрема Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila та Snigdha Agarwal
+**🙏 Особлива подяка 🙏 авторам, рецензентам і контент-учасникам серед послів Microsoft Student Ambassador**, зокрема Рішіту Даглі, Мухаммаду Сакібу Хану Інан, Рохану Раджу, Александру Петреску, Абхішеку Джайсвалу, Наврін Табассум, Іоану Самуїлі та Снігді Аґарвал
 
-**🤩 Додаткова подяка студентським амбасадорам Microsoft Eric Wanjau, Jasleen Sondhi та Vidushi Gupta за наші уроки R!**
+**🤩 Додаткова вдячність послам Microsoft Student Ambassadors Еріку Ванджау, Джаслін Сонді та Відуші Гупта за уроки на R!**
 
 # Початок роботи
 
-Дотримуйтесь цих кроків:
-1. **Fork the Repository**: Натисніть кнопку "Fork" у верхньому правому куті цієї сторінки.
-2. **Clone the Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+Виконайте ці кроки:
+1. **Форкніть репозиторій**: Натисніть кнопку "Fork" у верхньому правому куті цієї сторінки.
+2. **Клонуйте репозиторій**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [знайдіть усі додаткові ресурси для цього курсу в нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [знайдіть всі додаткові ресурси цього курсу у нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Потрібна допомога?** Перевірте наш [Посібник з вирішення проблем](TROUBLESHOOTING.md) для вирішення поширених проблем із встановленням, налаштуванням та запуском уроків.
+> 🔧 **Потрібна допомога?** Перевірте наш [Посібник з усунення несправностей](TROUBLESHOOTING.md) для розв’язання типових проблем з інсталяцією, налаштуванням і запуском уроків.
 
 
-**[Студенти](https://aka.ms/student-page)**, щоб використовувати цю навчальну програму, форкніть увесь репозиторій на свій GitHub-акаунт і виконуйте вправи самостійно або в групі:
+**[Студенти](https://aka.ms/student-page)**, щоб використовувати цю навчальну програму, форкніть весь репозиторій у свій GitHub акаунт та виконуйте вправи самостійно або в групі:
 
-- Почніть з опитування перед лекцією.
-- Прочитайте лекцію та виконайте активності, зупиняючись і розмірковуючи під час кожної перевірки знань.
-- Намагайтеся створювати проекти, розуміючи уроки, замість того щоб просто запускати код рішення; однак цей код доступний у папках `/solution` у кожному уроці, орієнтованому на проект.
-- Пройдіть опитування після лекції.
-- Виконайте челендж.
+- Почніть із тесту перед лекцією.
+- Прочитайте лекцію й виконайте завдання, зупиняючись для роздумів на кожній перевірці знань.
+- Намагайтеся створювати проекти, розуміючи уроки, а не просто запускаючи код з рішеннями; однак цей код доступний у папках `/solution` для кожного проектно-орієнтованого уроку.
+- Зробіть тест після лекції.
+- Виконайте виклик.
 - Виконайте завдання.
-- Після завершення групи уроків відвідайте [Дошку обговорень](https://github.com/microsoft/ML-For-Beginners/discussions) і «вчіться вголос», заповнивши відповідну рубрику PAT. 'PAT' — це інструмент оцінки прогресу, який ви заповнюєте, щоб поглибити своє навчання. Ви також можете реагувати на інші PAT, щоб ми могли вчитися разом.
+- Після завершення групи уроків відвідайте [Дошку обговорень](https://github.com/microsoft/ML-For-Beginners/discussions) і "навчайтеся голосно", заповнюючи відповідну рубрику PAT. PAT — це інструмент оцінки прогресу, який ви заповнюєте для подальшого навчання. Ви також можете реагувати на інші PAT, щоб навчатися разом.
 
-> Для подальшого вивчення ми рекомендуємо слідувати цим модулям і навчальним шляхам на [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
+> Для подальшого навчання рекомендуємо проходити ці [модулі та навчальні шляхи Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
 
-**Викладачі**, ми включили деякі поради у [for-teachers.md](for-teachers.md) щодо того, як використовувати цю навчальну програму.
+**Вчителі**, ми [надали кілька порад](for-teachers.md) щодо використання цієї навчальної програми.
 
 ---
 
 ## Відеоогляди
 
-Деякі уроки доступні у вигляді коротких відео. Ви можете знайти їх вбудованими в уроки або в [плейлисті ML for Beginners на каналі Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos), натиснувши на зображення нижче.
+Деякі уроки доступні у короткому відеоформаті. Ви можете знайти їх безпосередньо в уроках або на [плейлисті ML for Beginners на каналі Microsoft Developer на YouTube](https://aka.ms/ml-beginners-videos), натиснувши на зображення нижче.
 
-[![Банер «ML для початківців»](../../translated_images/uk/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/uk/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Знайомство з командою
+## Знайомтесь з командою
 
-[![Промо-відео](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**Гіф від** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**Гіф створений** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Натисніть на зображення вище, щоб переглянути відео про проєкт та людей, які його створили!
+> 🎥 Натисніть на зображення вище для перегляду відео про проект і людей, які його створили!
 
 ---
 
 ## Педагогіка
 
-Ми обрали два педагогічні принципи під час створення цієї навчальної програми: забезпечити практичну, проектно-орієнтовану структуру та включити часті опитування. Крім того, ця програма має спільну **тему**, щоб надати їй цілісності.
+Під час розробки цієї навчальної програми ми обрали два педагогічні принципи: забезпечити практичний **проектно-орієнтований підхід** і включити **часті тести**. Окрім цього, програма має спільну **тему** для кращої узгодженості.
 
-Забезпечивши відповідність контенту проєктам, процес стає більш цікавим для студентів, а засвоєння концепцій — кращим. До того ж, низькостресове опитування перед заняттям спрямовує увагу студента на вивчення теми, а друге опитування після заняття забезпечує подальше закріплення знань. Ця програма розроблена так, щоб бути гнучкою та цікавою, її можна проходити повністю або частинами. Проєкти починаються з малого і стають складнішими до кінця 12-тижневого циклу. Програма також містить епілог про застосування ML у реальному світі, який можна використати як додаткові бали або як основу для обговорення.
+Забезпечуючи відповідність контенту проектам, процес навчання стає більш захопливим для студентів, а засвоєння концепцій покращується. Крім того, тест із низькою ставкою перед заняттям налаштовує студента на вивчення теми, тоді як другий тест після заняття забезпечує подальше запам’ятовування. Цю програму розроблено гнучкою та цікавою, її можна проходити повністю або частинами. Проекти починаються з простих і ускладнюються до кінця 12-тижневого циклу. Програма також містить епілог про реальні застосування машинного навчання, який можна використовувати для додаткових балів або для обговорення.
 
-> Знайдіть наші настанови: [Кодекс поведінки](CODE_OF_CONDUCT.md), [Внесення змін](CONTRIBUTING.md), [Переклад](TRANSLATIONS.md) та [Вирішення проблем](TROUBLESHOOTING.md). Ми вітаємо ваш конструктивний відгук!
+> Знайдіть наші правила [Кодексу поведінки](CODE_OF_CONDUCT.md), [Співпраці](CONTRIBUTING.md), [Перекладу](TRANSLATIONS.md) та [Усунення несправностей](TROUBLESHOOTING.md). Ми радо приймаємо конструктивний відгук!
 
 ## Кожен урок включає
 
-- необов'язкову скетчноут
-- необов'язкове додаткове відео
-- відеопроходження (лише деякі уроки)
-- [опитування-розминка перед лекцією](https://ff-quizzes.netlify.app/en/ml/)
+- необов’язкову скетчноуту
+- необов’язкове додаткове відео
+- відеоогляд (лише для деяких уроків)
+- [тест для розігріву перед лекцією](https://ff-quizzes.netlify.app/en/ml/)
 - письмовий урок
-- для уроків, орієнтованих на проєкти, покрокові інструкції зі створення проєкту
+- для проектно-орієнтованих уроків — покрокові інструкції зі створення проекту
 - перевірки знань
-- челендж
+- виклик
 - додаткову літературу
-- домашнє завдання
-- [опитування після лекції](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Примітка щодо мов**: Ці уроки головним чином написані на Python, але багато з них також доступні на R. Щоб виконати урок на R, перейдіть до папки `/solution` і шукайте уроки на R. Вони містять розширення .rmd, яке позначає файл **R Markdown**, який можна просто визначити як вбудовування `code chunks` (написаних на R або інших мовах) та `YAML header` (який керує форматом виводу, наприклад PDF) у `Markdown document`. Як такий, він слугує прикладною авторською рамкою для науки про дані, оскільки дозволяє поєднувати ваш код, його вивід та ваші думки, записуючи їх у Markdown. Крім того, документи R Markdown можна відтворювати у формати виводу, такі як PDF, HTML або Word.
-
-> **Примітка щодо опитувань**: Всі опитування містяться в папці [Quiz App folder](../../quiz-app), всього 52 опитування по три запитання у кожному. Вони пов'язані з уроків, але додаток опитувань можна запустити локально; дотримуйтесь інструкцій у папці `quiz-app`, щоб розгорнути локально або в Azure.
-
-| Номер уроку |                             Тема                              |                   Групування уроків                   | Навчальні цілі                                                                                                             |                                                              Пов'язаний урок                                                               |                        Автор                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Вступ до машинного навчання                |      [Вступ](1-Introduction/README.md)       | Ознайомтеся з базовими поняттями машинного навчання                                                                                |                                             [Урок](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Історія машинного навчання                 |      [Вступ](1-Introduction/README.md)       | Дізнайтеся про історію цієї галузі                                                                                         |                                            [Урок](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 Справедливість і машинне навчання                  |      [Вступ](1-Introduction/README.md)       | Які важливі філософські питання щодо справедливості мають враховувати студенти при побудові та застосуванні моделей МН? |                                              [Урок](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Техніки машинного навчання                 |      [Вступ](1-Introduction/README.md)       | Які методи використовують дослідники МН для побудови моделей?                                                                       |                                          [Урок](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Вступ до регресії                   |        [Регресія](2-Regression/README.md)         | Почніть працювати з Python та Scikit-learn для регресійних моделей                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Ціни на гарбузи Північної Америки 🎃                |        [Регресія](2-Regression/README.md)         | Візуалізуйте та очистіть дані для підготовки до МН                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Ціни на гарбузи Північної Америки 🎃                |        [Регресія](2-Regression/README.md)         | Побудова лінійних та поліноміальних регресійних моделей                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                Ціни на гарбузи Північної Америки 🎃                |        [Регресія](2-Regression/README.md)         | Побудуйте модель логістичної регресії                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Веб-додаток 🔌                          |           [Веб-додаток](3-Web-App/README.md)            | Створіть веб-додаток для використання вашої натренованої моделі                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Вступ до класифікації                 |    [Класифікація](4-Classification/README.md)     | Очистіть, підготуйте та візуалізуйте дані; вступ до класифікації                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             Смачні азійські та індійські страви 🍜             |    [Класифікація](4-Classification/README.md)     | Вступ до класифікаторів                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
-|      12       |             Смачні азійські та індійські страви 🍜             |    [Класифікація](4-Classification/README.md)     | Додаткові класифікатори                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
-|      13       |             Смачні азійські та індійські страви 🍜             |    [Класифікація](4-Classification/README.md)     | Створіть рекомендаційний веб-додаток, використовуючи вашу модель                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Вступ до кластеризації                   |        [Кластеризація](5-Clustering/README.md)         | Очистіть, підготуйте та візуалізуйте дані; вступ до кластеризації                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Дослідження музичних вподобань Нігерії 🎧              |        [Кластеризація](5-Clustering/README.md)         | Ознайомтеся з методом кластеризації K-Means                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Вступ до обробки природної мови ☕️         |   [Обробка природної мови](6-NLP/README.md)    | Вивчіть основи NLP, створивши простого бота                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Поширені задачі NLP ☕️                      |   [Обробка природної мови](6-NLP/README.md)    | Поглибте знання з NLP, розуміючи типові задачі, що виникають при роботі з мовними структурами                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Переклад і аналіз тональності ♥️              |   [Обробка природної мови](6-NLP/README.md)    | Переклад і аналіз тональності на прикладі творів Джейн Остін                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Романтичні готелі Європи ♥️                  |   [Обробка природної мови](6-NLP/README.md)    | Аналіз тональності з відгуків про готелі 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Романтичні готелі Європи ♥️                  |   [Обробка природної мови](6-NLP/README.md)    | Аналіз тональності з відгуків про готелі 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Вступ до прогнозування часових рядів             |        [Часові ряди](7-TimeSeries/README.md)        | Вступ до прогнозування часових рядів                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Світове споживання електроенергії ⚡️ - прогнозування часових рядів з ARIMA |        [Часові ряди](7-TimeSeries/README.md)        | Прогнозування часових рядів з ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Світове споживання електроенергії ⚡️ - прогнозування часових рядів зі SVR  |        [Часові ряди](7-TimeSeries/README.md)        | Прогнозування часових рядів за допомогою Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Вступ до підкріплювального навчання             | [Підкріплювальне навчання](8-Reinforcement/README.md) | Вступ до підкріплювального навчання з Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Допоможіть Пітеру уникнути вовка! 🐺                  | [Підкріплювальне навчання](8-Reinforcement/README.md) | Gym для підкріплювального навчання                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            Сценарії та застосування ML у реальному світі            |      [ML у реальному світі](9-Real-World/README.md)       | Цікавi та показові реальні застосування класичного МН                                                               |                                             [Урок](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            Налагодження моделей у ML за допомогою панелі RAI          |      [ML у реальному світі](9-Real-World/README.md)       | Налагодження моделей машинного навчання з використанням компонентів панелі Responsible AI                                                              |                                             [Урок](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [Знайдіть усі додаткові ресурси для цього курсу в нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-## Офлайн доступ
-
-Ви можете запускати цю документацію офлайн, використовуючи [Docsify](https://docsify.js.org/#/). Зробіть форк цього репозиторію, [встановіть Docsify](https://docsify.js.org/#/quickstart) на вашому локальному комп'ютері, а потім у кореневій папці цього репозиторію введіть `docsify serve`. Вебсайт буде доступний на порту 3000 на вашому localhost: `localhost:3000`.
-
-## PDF-файли
-
-Знайдіть PDF навчальної програми з посиланнями [тут](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+- завдання
+- [тест після лекції](https://ff-quizzes.netlify.app/en/ml/)
+
+> **Примітка щодо мов:** Ці уроки переважно написані на Python, але багато з них також доступні на R. Щоб виконати урок на R, перейдіть до папки `/solution` і знайдіть уроки на R. Вони мають розширення .rmd, що означає **R Markdown** файл, який можна описати як вбудування `фрагментів коду` (на R або інших мовах) та `YAML-заголовка` (який керує форматом виводу, наприклад PDF) у `Markdown-документ`. Це слугує прекрасним авторським інструментом для data science, оскільки дозволяє поєднувати код, його вивід і ваші думки, записуючи їх у Markdown. Крім того, документи R Markdown можна експортувати у формати PDF, HTML або Word.
+> **Примітка щодо вікторин**: Всі вікторини містяться у [папці Quiz App](../../quiz-app), загалом 52 вікторини по три питання в кожній. Вони пов’язані з уроками, але додаток для вікторин можна запускати локально; дотримуйтесь інструкцій у папці `quiz-app` для локального розгортання або розміщення в Azure.
+
+| Номер уроку |                             Тема                              |                   Група уроків                   | Навчальні цілі                                                                                                             |                                                              Пов’язаний урок                                                               |                        Автор                        |
+| :---------: | :------------------------------------------------------------: | :----------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------: |
+|      01     |                Вступ до машинного навчання                    |      [Introduction](1-Introduction/README.md)   | Вивчити базові концепції машинного навчання                                                                              |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                            |                       Muhammad                      |
+|      02     |                Історія машинного навчання                      |      [Introduction](1-Introduction/README.md)   | Вивчити історію цієї галузі                                                                                                |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                           |                     Jen and Amy                      |
+|      03     |                 Справедливість та машинне навчання             |      [Introduction](1-Introduction/README.md)   | Які важливі філософські питання справедливості слід враховувати при створенні та застосуванні моделей ML?                  |                                              [Lesson](1-Introduction/3-fairness/README.md)                                              |                        Tomomi                       |
+|      04     |                Техніки машинного навчання                      |      [Introduction](1-Introduction/README.md)   | Які техніки використовують дослідники ML для створення моделей?                                                           |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                          |                    Chris and Jen                     |
+|      05     |                   Вступ до регресії                            |        [Regression](2-Regression/README.md)     | Початок роботи з Python та Scikit-learn для моделей регресії                                                             |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)          |      Jen • Eric Wanjau       |
+|      06     |                Ціни на гарбузи Північної Америки 🎃             |        [Regression](2-Regression/README.md)     | Візуалізація та очищення даних перед ML                                                                                   |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07     |                Ціни на гарбузи Північної Америки 🎃             |        [Regression](2-Regression/README.md)     | Побудова лінійних та поліноміальних моделей регресії                                                                      |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
+|      08     |                Ціни на гарбузи Північної Америки 🎃             |        [Regression](2-Regression/README.md)     | Побудова моделі логістичної регресії                                                                                      |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09     |                          Веб-додаток 🔌                         |           [Web App](3-Web-App/README.md)         | Створити веб-додаток для використання навченої моделі                                                                      |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10     |                 Вступ до класифікації                          |    [Classification](4-Classification/README.md) | Очищення, підготовка та візуалізація даних; вступ до класифікації                                                           | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)| Jen and Cassie • Eric Wanjau |
+|      11     |             Смачна азіатська та індійська кухня 🍜              |    [Classification](4-Classification/README.md) | Вступ до класифікаторів                                                                                                    | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|      12     |             Смачна азіатська та індійська кухня 🍜              |    [Classification](4-Classification/README.md) | Більше класифікаторів                                                                                                      | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
+|      13     |             Смачна азіатська та індійська кухня 🍜              |    [Classification](4-Classification/README.md) | Створення веб-додатку рекомендацій з використанням моделі                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14     |                   Вступ до кластеризації                       |        [Clustering](5-Clustering/README.md)      | Очищення, підготовка та візуалізація даних; вступ до кластеризації                                                          |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)        |      Jen • Eric Wanjau       |
+|      15     |              Дослідження музичних смаків Нігерії 🎧             |        [Clustering](5-Clustering/README.md)      | Дослідження методу кластеризації K-середніх                                                                               |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|      16     |        Вступ до обробки природної мови ☕️                      |   [Natural language processing](6-NLP/README.md) | Вивчити основи NLP, створюючи простого бота                                                                               |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17     |                      Поширені завдання NLP ☕️                  |   [Natural language processing](6-NLP/README.md) | Поглибити знання NLP, зрозумівши типові завдання при роботі з мовними структурами                                         |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      18     |             Переклад та аналіз настроїв ♥️                      |   [Natural language processing](6-NLP/README.md) | Переклад і аналіз настроїв з Jane Austen                                                                                 |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|      19     |                  Романтичні готелі Європи ♥️                   |   [Natural language processing](6-NLP/README.md) | Аналіз настроїв на основі відгуків про готелі 1                                                                            |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|      20     |                  Романтичні готелі Європи ♥️                   |   [Natural language processing](6-NLP/README.md) | Аналіз настроїв на основі відгуків про готелі 2                                                                            |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|      21     |            Вступ до прогнозування часових рядів                |        [Time series](7-TimeSeries/README.md)     | Вступ до прогнозування часових рядів                                                                                       |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      22     | ⚡️ Споживання електроенергії у світі ⚡️ - прогноз з ARIMA    |        [Time series](7-TimeSeries/README.md)     | Прогнозування часових рядів за допомогою ARIMA                                                                             |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|      23     |  ⚡️ Споживання електроенергії у світі ⚡️ - прогноз зі SVR    |        [Time series](7-TimeSeries/README.md)     | Прогнозування часових рядів за допомогою регресора опорних векторів                                                       |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|      24     |             Вступ до підкріплювального навчання               | [Reinforcement learning](8-Reinforcement/README.md) | Вступ до підкріплювального навчання з Q-Learning                                                                             |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|      25     |                 Допоможіть Пітеру уникнути вовка! 🐺          | [Reinforcement learning](8-Reinforcement/README.md) | Підкріплювальне навчання Gym                                                                                              |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  Постскрипт  |            Реальні сценарії та застосування ML                 |      [ML in the Wild](9-Real-World/README.md)    | Цікаві і показові реальні застосування класичного ML                                                                        |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
+|  Постскрипт  |            Відлагодження моделей ML за допомогою панелі RAI    |      [ML in the Wild](9-Real-World/README.md)    | Відлагодження моделей машинного навчання з використанням панелі Responsible AI                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+
+> [знайти всі додаткові ресурси для цього курсу у нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
+## Оффлайн доступ
+
+Ви можете запускати цю документацію оффлайн за допомогою [Docsify](https://docsify.js.org/#/). Форкніть цей репозиторій, [встановіть Docsify](https://docsify.js.org/#/quickstart) на свій локальний комп’ютер, а потім у кореневій папці цього репозиторію введіть `docsify serve`. Вебсайт буде доступний на порті 3000 на вашому локальному хості: `localhost:3000`.
+
+## PDFs
+
+Знайдіть pdf навчальної програми з посиланнями [тут](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
 ## 🎒 Інші курси 
@@ -167,57 +176,57 @@ Cloud Advocates у Microsoft раді запропонувати 12-тижнев
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j для початківців](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js для початківців](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 
 ---
 
-### Azure / Edge / MCP / Agents
+### Azure / Edge / MCP / Агентів
 [![AZD для початківців](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Edge AI для початківців](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![MCP для початківців](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents для початківців](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Агенти для початківців](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Generative AI Series
-[![Generative AI для початківців](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+### Серія Generative AI
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![Генеративний ШІ (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![Генеративний ШІ (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Основне навчання
-[![Машинне навчання для початківців](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![Наука про дані для початківців](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![ШІ для початківців](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![Кібербезпека для початківців](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![Веб-розробка для початківців](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![IoT для початківців](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR-розробка для початківців](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Серія Copilot
-[![Copilot для парного програмування зі ШІ](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![Copilot для C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Пригоди Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## Отримати допомогу
+## Отримання допомоги
 
-Якщо ви застрягли або маєте запитання щодо створення додатків ШІ. Приєднуйтесь до інших учнів та досвідчених розробників у обговореннях MCP. Це підтримуюча спільнота, де питання вітаються, а знання поширюються вільно.
+Якщо ви застрягли або маєте питання щодо створення AI-додатків. Приєднуйтесь до інших учнів та досвідчених розробників у дискусіях про MCP. Це підтримуюча спільнота, де питання вітаються, а знання вільно поширюються.
 
-[![Microsoft Foundry у Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Якщо у вас є відгуки про продукт або ви натрапили на помилки під час розробки, відвідайте:
+Якщо у вас є відгуки про продукт або помилки під час створення, відвідайте:
 
-[![Форум розробників Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-Відмова від відповідальності:
-Цей документ було перекладено за допомогою сервісу перекладу на основі ШІ [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, просимо врахувати, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується звернутися до професійного перекладача. Ми не несемо відповідальності за будь-які непорозуміння чи неправильні тлумачення, що виникли внаслідок використання цього перекладу.
+**Відмова від відповідальності**:
+Цей документ було перекладено за допомогою сервісу автоматичного перекладу AI [Co-op Translator](https://github.com/Azure/co-op-translator). Хоч ми і прагнемо до точності, будь ласка, майте на увазі, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ його рідною мовою слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний переклад людиною. Ми не несемо відповідальності за будь-які непорозуміння чи неправильні тлумачення, що виникли внаслідок використання цього перекладу.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
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