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diff --git a/translated_images/uk/.co-op-translator.json b/translated_images/uk/.co-op-translator.json
index 49560804d..6250b680e 100644
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diff --git a/translations/et/README.md b/translations/et/README.md
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-[![GitHub litsents](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub kaastöötajad](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub probleemid](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
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-[![PR-id on teretulnud](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)
 
-[![Microsoft Foundryi Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
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-[![GitHub jälgijad](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub forkid](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub tähed](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/)
 
 ### 🌐 Mitmekeelne tugi
 
-#### Toetatud GitHub Actioni kaudu (automatiseeritud ja alati ajakohane)
+#### Toetatud GitHub Actioni kaudu (automatiseeritud & alati värske)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Araabia](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgaaria](../bg/README.md) | [Birmani (Myanmar)](../my/README.md) | [Hiina (lihtsustatud)](../zh/README.md) | [Hiina (traditsiooniline, Hongkong)](../hk/README.md) | [Hiina (traditsiooniline, Macao)](../mo/README.md) | [Hiina (traditsiooniline, Taiwan)](../tw/README.md) | [Horvaadi](../hr/README.md) | [Tšehhi](../cs/README.md) | [Taani](../da/README.md) | [Hollandi](../nl/README.md) | [Eesti](./README.md) | [Soome](../fi/README.md) | [Prantsuse](../fr/README.md) | [Saksa](../de/README.md) | [Kreeka](../el/README.md) | [Heebrea](../he/README.md) | [Hindi](../hi/README.md) | [Ungari](../hu/README.md) | [Indoneesia](../id/README.md) | [Itaalia](../it/README.md) | [Jaapani](../ja/README.md) | [Kannada](../kn/README.md) | [Korea](../ko/README.md) | [Leedu](../lt/README.md) | [Malai](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigeeria pidgin](../pcm/README.md) | [Norra](../no/README.md) | [Pärsia (farsi)](../fa/README.md) | [Poola](../pl/README.md) | [Portugali (Brasiilia)](../br/README.md) | [Portugali (Portugal)](../pt/README.md) | [Pandžabi (Gurmukhi)](../pa/README.md) | [Rumeenia](../ro/README.md) | [Vene](../ru/README.md) | [Serbia (kirilitsa)](../sr/README.md) | [Slovaki](../sk/README.md) | [Sloveenia](../sl/README.md) | [Hispaania](../es/README.md) | [Suahiili](../sw/README.md) | [Rootsi](../sv/README.md) | [Tagalogi (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tai](../th/README.md) | [Türgi](../tr/README.md) | [Ukraina](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnami](../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](./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)
+
+> **Eelistad kloonida kohapeal?**
+
+> See hoidla sisaldab üle 50 keele tõlkeid, mis suurendab märkimisväärselt allalaadimise mahtu. Tõlgeteta kloonimiseks kasuta harvendatud (sparse) checkouti:
+> ```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'
+> ```
+> See annab sulle kõik vajaliku kursuse läbimiseks palju kiirema allalaadimisega.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### Liitu meie kogukonnaga
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Meil on käimas Discordi sarja "Õpi tehisintellektiga"; saa rohkem teada ja liitu meiega aadressil [Õpi tehisintellektiga sari](https://aka.ms/learnwithai/discord) ajavahemikul 18 - 30 September, 2025. Saad näpunäiteid ja trikke GitHub Copiloti kasutamiseks andmeteaduses.
+Meil on käimas Discordi õppesari "Õpi koos tehisintellektiga", uuri lisa ja liitu meiega aadressil [Learn with AI Series](https://aka.ms/learnwithai/discord) ajavahemikus 18. - 30. september 2025. Saad saada vihjeid ja nippe GitHub Copiloti kasutamiseks andmeteaduses.
 
-![Õpi tehisintellektiga](../../translated_images/et/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/et/3.9b58fd8d6c373c20.webp)
 
-# Masinõpe algajatele - Õppekava
+# Masinõpe algajatele - õppekava
 
-> 🌍 Rändame ümber maailma, kui uurime masinõpet läbi maailma kultuuride 🌍
+> 🌍 Rändame mööda maailma, uurides masinõpet erinevate maailmakultuuride kaudu 🌍
 
-Microsofti Cloud Advocates meeskond on rõõmus pakkuda 12-nädalast, 26-õppetunnilist õppekava, mis käsitleb **masinõpet**. Selles õppekavas õpid nii-öelda **klassikalist masinõpet**, kasutades peamiselt Scikit-learn raamatukogu ja vältides süvaõpet, mida käsitletakse meie [AI for Beginners' curriculum](https://aka.ms/ai4beginners) kursusel. Kaasa nendele õppetundidele meie ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners).
+Microsofti pilveettekandjate meeskond pakub 12-nädalast, 26-õppetunnist koosnevat õppekava, mis keskendub **masinõppele**. Selles õppekavas õpid nn **klassikalist masinõpet**, kasutades peamiselt Scikit-learn kauplust ning vältides süvaõpet, mida käsitletakse meie [tehisintellekti algajatele õppekavas](https://aka.ms/ai4beginners). Paarita need õppetunnid ka meie ['Andmeteaduse algajatele' õppekavaga](https://aka.ms/ds4beginners)!
 
-Rända meiega üle maailma, kui rakendame neid klassikalisi tehnikaid eri piirkondade andmetele. Iga õppetund sisaldab eelloengu ja järel-õppe teste, kirjalikke juhiseid õppetunni läbimiseks, lahendust, ülesannet ja palju muud. Meie projektipõhine õpetamismeetod võimaldab sul õppida ehitades — tõestatud viis uute oskuste kinnistamiseks.
+Rändame koos üle maailma, rakendades neid klassikalisi meetodeid erinevate maailma piirkondade andmetele. Iga õppetund sisaldab eelkatsed ja järeltöötluskatsed, kirjalikud juhised õppetöö lõpetamiseks, lahenduse, tööülesande ja veelgi rohkem. Meie projektipõhine pedagoogika võimaldab sul õppida ehitades, mis on tõestatud viis uute oskuste kinnistamiseks.
 
-**✍️ Suur tänu meie autoritele** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ja Amy Boyd
+**✍️ Südamlik tänu meie autoritele** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ja Amy Boyd
 
 **🎨 Tänud ka meie illustraatoritele** Tomomi Imura, Dasani Madipalli ja Jen Looper
 
-**🙏 Eriline tänu 🙏 meie Microsoft Student Ambassador autoritele, läbivaatajaile ja sisuloojad**, eelkõige Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ja Snigdha Agarwal
+**🙏 Eriline tänu 🙏 meie Microsofti õpilas-suursaadikute autoritele, arvustajatele ja sisuloojatele**, eriti Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ja Snigdha Agarwal
 
-**🤩 Täiendav tänu Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi ja Vidushi Gupta meie R-õppetundide eest!**
+**🤩 Täiendav tänu Microsofti õpilas-suursaadikutele Eric Wanjau, Jasleen Sondhi ja Vidushi Gupta meie R-õppetundide eest!**
 
 # Alustamine
 
-Järgnevaid samme:
-1. **Forki hoidla**: Klõpsa selle lehe paremas ülanurgas nuppu "Fork".
+Järgi neid samme:
+1. **Tee hoidla fork**: kliki selle lehe paremas ülanurgas olevale nupule „Fork“.
 2. **Klooni hoidla**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [leiate kõik täiendavad ressursid selle kursuse jaoks meie Microsoft Learn kogust](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [leiad kõik täiendavad selle kursuse ressursid meie Microsoft Learn kollektsioonist](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Vajad abi?** Vaata meie [Tõrkeotsingu juhendit](TROUBLESHOOTING.md), et leida lahendusi levinud probleemidele installi, seadistuse ja õppetundide käivitamisega.
+> 🔧 **Vajad abi?** Vaata meie [Tõrkeotsingu juhendit](TROUBLESHOOTING.md), kus on lahendused levinud probleemidele paigaldamisel, häälestamisel ja õppetundide käivitamisel.
 
-**[Õpilased](https://aka.ms/student-page)**, selle õppekava kasutamiseks, forkige kogu repositoorium omaenda GitHubi kontole ja täitke harjutusi iseseisvalt või rühmas:
 
-- Alustage eelloengu soojendus-küsimustikuga.
-- Lugege loengut ja täitke tegevused, peatuge ja mõtisklege igal teadmiste kontrollpunktil.
-- Proovige luua projektid, mõistes õppetunde, mitte ainult käivitades lahenduse koodi; see kood on siiski saadaval iga projekti-lähenemisele orienteeritud õppetunni `/solution` kaustades.
-- Tehke järel-õppe viktoriin.
-- Täitke väljakutse.
-- Täitke ülesanne.
-- Pärast õpperühma lõpetamist külastage [Arutelufoorumit](https://github.com/microsoft/ML-For-Beginners/discussions) ja "õppige valjult", täites vastava PAT hindamisrubriigi. 'PAT' on Progress Assessment Tool (edusamme hindav tööriist), mille kaudu täidetakse rubriik, et edendada oma õppimist. Võite ka reageerida teiste PAT-idele, et üheskoos õppida.
+**[Õpilased](https://aka.ms/student-page)**, selle õppekava kasutamiseks tee fork kogu hoidlast omaenda GitHubi kontole ja lõpeta harjutused iseseisvalt või grupiga:
 
-> Lisauurimiseks soovitame järgida neid [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) mooduleid ja õppeplaane.
+- Alusta eelkatsest.
+- Loe loeng ja lõpeta tegevused, peatu ja mõtiskle igal teadmiste kontrollimisel.
+- Püüa luua projektid, mõistes õppetunde, selle asemel et koodi kopeerida; kuid see kood on olemas iga projektimeelse õppetunni /solution kaustas.
+- Tee pärast loengut test.
+- Tee väljakutse.
+- Tee tööülesanne.
+- Pärast õppetunde rühma lõpetamist külasta [Arutelufoorumit](https://github.com/microsoft/ML-For-Beginners/discussions) ja „õpi valjusti“ läbides sobiva PAT hindamislehe. PAT on edenemise hindamiseks kasutatav rubriik, mida täites oma õppimist toetad. Samuti võid reageerida teiste PATidele, et koos õppida.
 
-**Õpetajad**, oleme lisanud [mõningaid soovitusi](for-teachers.md), kuidas seda õppekava kasutada.
+> Täiendavaks õppeks soovitame järgida neid [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) mooduleid ja õpiteid.
+
+**Õpetajad**, meil on [mõningaid soovitusi](for-teachers.md), kuidas seda õppekava kasutada.
 
 ---
 
-## Video läbivaated
+## Videokäivitused
 
-Mõned õppetunnid on saadaval lühivormis videote kujul. Leiate need integreeritult õppetundidest või [ML for Beginners esitusloendist Microsoft Developer YouTube kanalil](https://aka.ms/ml-beginners-videos) pildi klõpsamisel allpool.
+Mõned õppetunnid on saadaval lühikeste videotena. Leiad need sisseehitatult õppetundidest või [ML algajatele esitusloendist Microsofti arendaja YouTube kanalil](https://aka.ms/ml-beginners-videos) pildile klõpsates.
 
-[![ML algajatele bänner](../../translated_images/et/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/et/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
 ## Tutvu meeskonnaga
 
-[![Tutvustusvideo](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**GIF autor** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**Gif autor** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Klõpsa ülalolevat pilti, et vaadata videot projektist ja inimestest, kes selle lõid!
+> 🎥 Klõpsa ülalolevale pildile, et vaadata videot projektist ja inimestest, kes selle lõid!
 
 ---
 
 ## Pedagoogika
 
-Selle õppekava koostamisel valisime kaks pedagoogilist printsiipi: tagada, et see on praktiline ja **projektipõhine**, ning et see sisaldab **sagedasi teste**. Lisaks on õppekaval ühine **teema**, mis annab sellele sidususe.
+Selle õppekava loomisel oleme valinud kaks pedagoogilist printsiipi: tagada, et see on praktiline **projektipõhine** ja sisaldab **sagedasi teste**. Lisaks on sellel õppekaval ühine **teema**, mis annab sellele sidususe.
 
-Sisu ühendades projektidega muutub protsess õpilaste jaoks haaravamaks ning kontseptsioonide meeldejätmine paraneb. Veelgi enam, madala panusega eelloengu viktoriin seab õpilase õppefookuse, samas kui teine viktoriin pärast loengut tagab teadmiste pikaajalisema kinnistumise. Seda õppekava on kavandatud olema paindlik ja lõbus ning seda saab läbida tervikuna või osade kaupa. Projektid algavad väikestena ja muutuvad 12-nädalase tsükli lõpuks järjest keerukamaks. Õppekavas on ka järelsõna masinõppe reaalses maailmas rakendustest, mida saab kasutada täiendava krediidi või arutelu alusena.
+Sisule projektide kaudu sobitamisega muudetakse protsess õppuritele huvitavamaks ja kontseptsioonide kinnistamine tugevamaks. Lisaks seab madala panusega eelkats enne tundi õppuri kavatsuse teemat omandada, samas kui teine kats tunnijärgselt tagab teadmiste kinnistamise. See õppekava on loodud olema paindlik ja lõbus ning seda saab läbida tervikuna või ositi. Projektid algavad väikestena ja muutuvad 12-nädalase tsükli lõpus järjest keerukamaks. Õppekava sisaldab ka järelsõna masinõppe reaalse maailma rakendustest, mida võib kasutada lisapunktide saamiseks või arutelupõhjana.
 
-> Leiate meie [käitumisreeglid](CODE_OF_CONDUCT.md), [panustamise juhised](CONTRIBUTING.md), [tõlkejuhised](TRANSLATIONS.md) ja [tõrkeotsingu juhendid](TROUBLESHOOTING.md). Ootame teie konstruktiivset tagasisidet!
+> Leia meie [käitumiskoodeks](CODE_OF_CONDUCT.md), [panustamine](CONTRIBUTING.md), [tõlkimine](TRANSLATIONS.md) ja [tõrkeotsing](TROUBLESHOOTING.md) juhendid. Ootame konstruktiivset tagasisidet!
 
-## Igas õppetunnis on
+## Iga õppetund sisaldab
 
-- valikuline sketchnote
-- valikuline lisavideo
-- video läbivaade (ainult mõnedes õppetundides)
-- [eelloengu soojendus-viktoriin](https://ff-quizzes.netlify.app/en/ml/)
-- kirjutatud õppetund
-- projektipõhiste õppetundide jaoks samm-sammult juhendid, kuidas projekti üles ehitada
+- vabatahtlik sketšmärkmed
+- vabatahtlik täiendav video
+- videotutvustus (ainult mõni õppetund)
+- [eelloengu soojenduskatse](https://ff-quizzes.netlify.app/en/ml/)
+- kirjalik õppetund
+- projektipõhiste õppetundide puhul samm-sammulised juhised, kuidas projekt üles ehitada
 - teadmiste kontrollid
 - väljakutse
-- lisa lugemine
-- ülesanne
-- [järgnev viktoriin](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Märkus keeltest**: Need õppetunnid on peamiselt kirjutatud Pythonis, kuid paljud on saadaval ka R-is. R-õppetunni läbimiseks minge `/solution` kausta ja otsige R-õppetunde. Need sisaldavad .rmd laiendit, mis tähistab **R Markdown** faili, mida võib lihtsalt määratleda kui `koodiblokkide` (R-i või teiste keelte) ja `YAML päise` (mis juhib väljundite, näiteks PDF, vormindamist) manustamist `Markdown` dokumendis. See toimib suurepärase autorlusraamistikuna andmeteadusele, sest lubab kombineerida koodi, selle väljundeid ja oma mõtteid, võimaldades neid Markdownis kirja panna. Lisaks saab R Markdown dokumente renderdada väljundiformaatidesse nagu PDF, HTML või Word.
-
-> **Märkus viktoriinide kohta**: Kõik viktoriinid asuvad [Quiz App kaustas](../../quiz-app), kokku 52 viktoriini, igaühes kolm küsimust. Need on lingitud õppetundide sisse, kuid viktoriini rakendust saab käivitada ka lokaalselt; järgige juhiseid `quiz-app` kaustas, et hostida lokaalselt või juurutada Azure'i.
-
-| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Sissejuhatus masinõppesse                |      [Sissejuhatus](1-Introduction/README.md)       | Tutvu masinõppe põhiliste mõistetega                                                                                |                                             [Õppetund](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Masinõppe ajalugu                 |      [Sissejuhatus](1-Introduction/README.md)       | Õpi selle valdkonna ajalugu                                                                                         |                                            [Õppetund](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen ja Amy                      |
-|      03       |                 Õiglus ja masinõpe                  |      [Sissejuhatus](1-Introduction/README.md)       | Millised on olulised filosoofilised küsimused seoses õiglusega, mida õpilased peaksid kaaluma masinõppemudelite loomisel ja rakendamisel? |                                              [Õppetund](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Masinõppe tehnikad                 |      [Sissejuhatus](1-Introduction/README.md)       | Milliseid tehnikaid kasutavad masinõppe uurijad mudelite ülesehitamiseks?                                                                       |                                          [Õppetund](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris ja Jen                     |
-|      05       |                   Sissejuhatus regressiooni                   |        [Regressioon](2-Regression/README.md)         | Alusta Pythoniga ja Scikit-learniga regressioonimudelite jaoks                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Põhja-Ameerika kõrvitsa hinnad 🎃                |        [Regressioon](2-Regression/README.md)         | Visualiseeri ja puhasta andmeid ettevalmistuseks masinõppeks                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Põhja-Ameerika kõrvitsa hinnad 🎃                |        [Regressioon](2-Regression/README.md)         | Ehita lineaarseid ja polünoomseid regressioonimudeleid                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen ja Dmitry • Eric Wanjau       |
-|      08       |                Põhja-Ameerika kõrvitsa hinnad 🎃                |        [Regressioon](2-Regression/README.md)         | Ehita logistiline regressioonimudel                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Veebirakendus 🔌                          |           [Veebirakendus](3-Web-App/README.md)            | Ehita veebirakendus treenitud mudeli kasutamiseks                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Sissejuhatus klassifitseerimisse                 |    [Klassifikatsioon](4-Classification/README.md)     | Puhasta, ettevalmista ja visualiseeri oma andmeid; sissejuhatus klassifikatsiooni                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen ja Cassie • Eric Wanjau |
-|      11       |             Maitsvad Aasia ja India köögid 🍜             |    [Klassifikatsioon](4-Classification/README.md)     | Sissejuhatus klassifikaatoritesse                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ja Cassie • Eric Wanjau |
-|      12       |             Maitsvad Aasia ja India köögid 🍜             |    [Klassifikatsioon](4-Classification/README.md)     | Rohkem klassifikaatoreid                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ja Cassie • Eric Wanjau |
-|      13       |             Maitsvad Aasia ja India köögid 🍜             |    [Klassifikatsioon](4-Classification/README.md)     | Ehita soovituste veebirakendus, kasutades oma mudelit                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Sissejuhatus klasterdamisse                   |        [Klasterdamine](5-Clustering/README.md)         | Puhasta, ettevalmista ja visualiseeri oma andmeid; sissejuhatus klasterdamisse                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Nigeeria muusikamaitse uurimine 🎧              |        [Klasterdamine](5-Clustering/README.md)         | Uuri K-Meansi klasterdamismeetodit                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Sissejuhatus loomuliku keele töötlemisse (NLP) ☕️         |   [Loomuliku keele töötlemine](6-NLP/README.md)    | Õpi NLP põhialuseid, ehitades lihtsa boti                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Tavalised NLP ülesanded ☕️                      |   [Loomuliku keele töötlemine](6-NLP/README.md)    | Sügavda oma NLP teadmisi, mõistes keelestruktuuridega töötamisel vajalikke tavapäraseid ülesandeid                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Tõlkimine ja sentimendianalüüs ♥️              |   [Loomuliku keele töötlemine](6-NLP/README.md)    | Tõlkimine ja sentimendianalüüs Jane Austeni tekstidega                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Euroopa romantilised hotellid ♥️                  |   [Loomuliku keele töötlemine](6-NLP/README.md)    | Sentimendianalüüs hotellide arvustustega 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Euroopa romantilised hotellid ♥️                  |   [Loomuliku keele töötlemine](6-NLP/README.md)    | Sentimendianalüüs hotellide arvustustega 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Sissejuhatus ajareadede prognoosimisse             |        [Ajaread](7-TimeSeries/README.md)        | Sissejuhatus ajareadede prognoosimisse                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Maailma elektrikasutus ⚡️ - ajareadede prognoosimine ARIMA abil |        [Ajaread](7-TimeSeries/README.md)        | Ajareadede prognoosimine ARIMA abil                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Maailma elektrikasutus ⚡️ - ajareadede prognoosimine SVR-iga  |        [Ajaread](7-TimeSeries/README.md)        | Ajareadede prognoosimine Support Vector Regressoriga                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Sissejuhatus tugevdusõppesse             | [Tugevdusõpe](8-Reinforcement/README.md) | Sissejuhatus tugevdusõppesse Q-õppe abil                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Aita Peteril hunda vältida! 🐺                  | [Tugevdusõpe](8-Reinforcement/README.md) | Tugevdusõppe Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Järelsõna   |            Reaalmaailma ML stsenaariumid ja rakendused            |      [ML in the Wild](9-Real-World/README.md)       | Huvitavad ja ilmekad reaalmaailma rakendused klassikalisest masinõppest                                                               |                                             [Õppetund](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Järelsõna   |            Mudeli silumine masinõppes RAI armatuuri abil          |      [ML in the Wild](9-Real-World/README.md)       | Masinõppe mudelite silumine, kasutades Responsible AI armatuuri komponente                                                              |                                             [Õppetund](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [Leia kõik selle kursuse täiendavad ressursid meie Microsoft Learni kollektsioonist](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-## Võrguühenduseta juurdepääs
-
-Sa võid seda dokumentatsiooni käivitada võrguühenduseta, kasutades [Docsify](https://docsify.js.org/#/). Tee selle hoidla fork, [paigalda Docsify](https://docsify.js.org/#/quickstart) oma kohalikule masinale ning seejärel selle hoidla juurkataloogis sisesta `docsify serve`. Veebileht on kättesaadav pordil 3000 sinu localhostis: `localhost:3000`.
+- täiendav lugemine
+- tööülesanne
+- [järguloengu test](https://ff-quizzes.netlify.app/en/ml/)
+
+> **Märkus keeltest**: Need õppetunnid on peamiselt kirjutatud Pythoni keeles, kuid paljud on saadaval ka R-s. R-õppetunni läbimiseks mine /solution kausta ja otsi R õppetunde. Need sisaldavad .rmd laiendit, mis tähistab **R Markdowni** faili, mida võib defineerida kui `koodiblokkide` (R või teiste keelte) ja `YAML päise` (mis juhib väljundite vormindamist, nt PDF) kombineerimist Markdown dokumendis. See pakub suurepärast raamistikku andmeteaduseks, sest võimaldab kombineerida koodi, selle väljundit ja mõtteid, mille saab kirja panna Markdownis. Lisaks saab R Markdowni dokumente renderdada väljundvormingutesse nagu PDF, HTML või Word.
+> **Märkus viktoriinide kohta**: Kõik viktoriinid asuvad [Quiz App kaustas](../../quiz-app), kokku 52 viktoriini, milles igas on kolm küsimust. Need on õppetundidesse lingitud, kuid viktoriini rakendust saab käivitada lokaalselt; juhised lokaalseks majutamiseks või Azure'i kasutuselevõtuks leiate `quiz-app` kaustast.
+
+| Õppetunni number |                             Teema                              |                   Õppetunni grupp                   | Õpieesmärgid                                                                                                                  |                                                              Link õppetundile                                                                |                        Autor                        |
+| :--------------: | :------------------------------------------------------------: | :-------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
+|       01         |                Masinõppe tutvustus                            |      [Sissejuhatus](1-Introduction/README.md)       | Õpi masinõppe põhikontseptsioone                                                                                            |                                             [Õppetund](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                      |
+|       02         |                Masinõppe ajalugu                              |      [Sissejuhatus](1-Introduction/README.md)       | Õpi selle valdkonna ajalugu                                                                                                 |                                            [Õppetund](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen ja Amy                       |
+|       03         |                 Õiglus ja masinõpe                            |      [Sissejuhatus](1-Introduction/README.md)       | Millised on olulised filosoofilised küsimused õiglusest, mida õppijad peaksid kaaluma masinõppemudelite loomisel ja kasutamisel? |                                                [Õppetund](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|       04         |                Masinõppe tehnikad                             |      [Sissejuhatus](1-Introduction/README.md)       | Milliseid tehnikaid masinõppe uurijad kasutavad mudelite loomiseks?                                                          |                                          [Õppetund](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris ja Jen                      |
+|       05         |                   Regresseerimise tutvustus                    |        [Regressioon](2-Regression/README.md)         | Alusta Pythoniga ja Scikit-learniga regressioonimudelite jaoks                                                               |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau        |
+|       06         |                Põhja-Ameerika kõrvitsa hinnad 🎃              |        [Regressioon](2-Regression/README.md)         | Visualiseeri ja puhasta andmeid masinõppe ettevalmistamiseks                                                                    |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau        |
+|       07         |                Põhja-Ameerika kõrvitsa hinnad 🎃              |        [Regressioon](2-Regression/README.md)         | Ehita lineaar- ja polünoomregressioonimudelid                                                                                 |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen ja Dmitry • Eric Wanjau        |
+|       08         |                Põhja-Ameerika kõrvitsa hinnad 🎃              |        [Regressioon](2-Regression/README.md)         | Ehita logistilise regressiooni mudel                                                                                           |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau        |
+|       09         |                          Veebirakendus 🔌                      |           [Veebirakendus](3-Web-App/README.md)       | Ära oma koolitatud mudeliga veebirakendus                                                                                      |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                           |
+|       10         |                 Klassifitseerimise tutvustus                   |    [Klassifitseerimine](4-Classification/README.md) | Puhasta, valmista ette ja visualiseeri oma andmeid; klassifitseerimise tutvustus                                               | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen ja Cassie • Eric Wanjau  |
+|       11         |             Maitsvad Aasia ja India toidud 🍜                  |    [Klassifitseerimine](4-Classification/README.md) | Klassifitseerijate tutvustus                                                                                                  | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ja Cassie • Eric Wanjau  |
+|       12         |             Maitsvad Aasia ja India toidud 🍜                  |    [Klassifitseerimine](4-Classification/README.md) | Rohkem klassifitseerijaid                                                                                                     | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ja Cassie • Eric Wanjau  |
+|       13         |             Maitsvad Aasia ja India toidud 🍜                  |    [Klassifitseerimine](4-Classification/README.md) | Ehita mudelit kasutav soovitusrakendus                                                                                        |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                           |
+|       14         |                   Klasterdamise tutvustus                      |        [Klasterdamine](5-Clustering/README.md)       | Puhasta, valmista ette ja visualiseeri oma andmeid; klasterdamise tutvustus                                                   |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau        |
+|       15         |              Nigeeria muusikamaitsete uurimine 🎧              |        [Klasterdamine](5-Clustering/README.md)       | Uuri K-keskmiste klasterdamismeetodit                                                                                        |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau        |
+|       16         |        Loodusliku keele töötlemise tutvustus ☕️               |   [Loodusliku keele töötlemine](6-NLP/README.md)     | Õpi NLP põhialuseid, ehitades lihtsa boti                                                                                     |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                         |
+|       17         |                      Levinumad NLP ülesanded ☕️               |   [Loodusliku keele töötlemine](6-NLP/README.md)     | Süvenda NLP teadmisi, mõistes keelestruktuuridega töötamiseks vajalikke ülesandeid                                            |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                         |
+|       18         |             Tõlge ja emotsioonianalüüs ♥️                      |   [Loodusliku keele töötlemine](6-NLP/README.md)     | Tõlge ja emotsioonianalüüs koos Jane Austeniga                                                                               |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                         |
+|       19         |                  Euroopa romantilised hotellid ♥️               |   [Loodusliku keele töötlemine](6-NLP/README.md)     | Emotsioonianalüüs hotellikriitikatega 1                                                                                       |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                         |
+|       20         |                  Euroopa romantilised hotellid ♥️               |   [Loodusliku keele töötlemine](6-NLP/README.md)     | Emotsioonianalüüs hotellikriitikatega 2                                                                                       |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                         |
+|       21         |            Ajajoonte prognoosimise tutvustus                   |        [Ajajoons](7-TimeSeries/README.md)             | Ajajoonte prognoosimise tutvustus                                                                                            |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                        |
+|       22         | ⚡️ Maailma energiatarve ⚡️ - ajajoonte prognoos ARIMA kaudu    |        [Ajajoons](7-TimeSeries/README.md)             | Ajajoonte prognoos ARIMA mudeliga                                                                                            |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                        |
+|       23         |  ⚡️ Maailma energiatarve ⚡️ - ajajoonte prognoos SVRiga        |        [Ajajoons](7-TimeSeries/README.md)             | Ajajoonte prognoos Toetava Vektori Regresseerijaga                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                         |
+|       24         |             Tugevdusõppe tutvustus                              | [Tugevdusõpe](8-Reinforcement/README.md)             | Tutvustus tugevdusõppele Q-õppe kaudu                                                                                        |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                         |
+|       25         |                 Aidake Peteril hundi eest põgeneda! 🐺         | [Tugevdusõpe](8-Reinforcement/README.md)             | Tugevdusõppe Gym                                                                                                             |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                         |
+|  Lisapeatükid  |            Masinõppe reaalsed stsenaariumid ja rakendused       |      [Masinõpe vabas looduses](9-Real-World/README.md) | Huvitavad ja valgustavad reaalsed masinõppe rakendused                                                                       |                                             [Õppetund](9-Real-World/1-Applications/README.md)                                              |                         Meeskond                      |
+|  Lisapeatükid  |            Mudelite silumine ML-s RAI juhtpaneeli abil         |      [Masinõpe vabas looduses](9-Real-World/README.md) | Masinõppe mudelite silumine kasutades Responsible AI juhtpaneeli komponente                                                  |                                             [Õppetund](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                    |
+
+> [leia kõik selle kursuse täiendavad ressursid meie Microsoft Learni kogumikust](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
+## Võrguühenduseta kasutamine
+
+Seda dokumentatsiooni saab kasutada võrguühenduseta, kasutades [Docsify](https://docsify.js.org/#/). Hargi see repositorium, [installi Docsify](https://docsify.js.org/#/quickstart) oma kohalikule masinale ja seejärel selle repositoriumi juurkaustas kirjuta `docsify serve`. Veebisait serverdatakse pordil 3000 sinu lokaalses hostis: `localhost:3000`.
 
 ## PDF-id
 
-Leia õppekava PDF koos linkidega [siit](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+Leia õppekava pdf koos linkidega [siit](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
-## 🎒 Teised kursused 
+## 🎒 Muud kursused
 
-Meie meeskond toodab ka teisi kursuseid! Vaata neid:
+Meie meeskond toodab ka teisi kursuseid! Vaata lähemalt:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j algajatele](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 algajatele](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
-[![AZD algajatele](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 algajatele](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 algajatele](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-agendid algajatele](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)
+### Azure / Edge / MCP / Agendid
+[![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 algajatele](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)
-[![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)
+
+### Generatiivne tehisintellekt seeria
+[![Generatiivne tehisintellekt algajatele](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)
+[![Generatiivne tehisintellekt (.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)
+[![Generatiivne tehisintellekt (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)
+[![Generatiivne tehisintellekt (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)
 
 ---
  
-### Põhiõpe
+### Põhijuhendamine
 [![Masinõpe algajatele](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)
 [![Andmeteadus algajatele](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)
 [![Tehisintellekt algajatele](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)
-[![Küberkaitse algajatele](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)
+[![Küberjulgeolek algajatele](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)
 [![Veebiarendus algajatele](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 algajatele](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 arendus algajatele](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)
+[![Asjade internet algajatele](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-arendus algajatele](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-seeria
-[![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)
+### Copiloti sari
+[![Copilot AI-paarprogrammeerimiseks](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 jaoks](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)
+[![Copiloti seiklused](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 -->
 
-## Abi
+## Abi saamine
 
-Kui jääd kinni või sul on küsimusi AI-rakenduste loomise kohta, liitu teiste õppijate ja kogenud arendajatega MCP aruteludes. See on toetav kogukond, kus küsimusi on oodatud ja teadmisi jagatakse vabalt.
+Kui jääd kinni või sul on küsimusi tehisintellekti rakenduste loomise kohta, liitu teiste õppijate ja kogenud arendajatega MCP aruteludes. See on toetav kogukond, kus küsimusi oodatakse ja teadmisi jagatakse vabalt.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Kui sul on toodete kohta tagasisidet või ehitamise ajal ilmnevaid vigu, külasta:
+Kui sul on toote tagasisidet või ehitamisel vigu, külasta:
 
 [![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 -->
-Vastutusest loobumine:
-See dokument on tõlgitud tehisintellektil põhineva tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi me püüame tagada täpsust, arvestage palun, et automatiseeritud tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokumenti selle algkeeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste ega vääritõlgenduste eest.
+**Vastutusest loobumine**:  
+See dokument on tõlgitud kasutades tehisintellektil põhinevat tõlketeenust [Co-op Translator](https://github.com/Azure/co-op-translator). Kuigi püüame tagada täpsuse, palun arvestage, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument selle emakeeles tuleb pidada autoriteetseks allikaks. Tähtsa teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tingitud arusaamatuste või valesti mõistmiste eest.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/pcm/1-Introduction/1-intro-to-ML/README.md b/translations/pcm/1-Introduction/1-intro-to-ML/README.md
index cab34c4c2..ec7f61fb8 100644
--- a/translations/pcm/1-Introduction/1-intro-to-ML/README.md
+++ b/translations/pcm/1-Introduction/1-intro-to-ML/README.md
@@ -42,7 +42,7 @@ Di word 'machine learning' na one of di popular and common words wey people dey
 ---
 ## Di hype curve
 
-![ml hype curve](../../../../translated_images/hype.07183d711a17aafe.pcm.png)
+![ml hype curve](../../../../translated_images/pcm/hype.07183d711a17aafe.webp)
 
 > Google Trends dey show di recent 'hype curve' of di word 'machine learning'
 
@@ -69,7 +69,7 @@ Even though di words fit confuse person, machine learning (ML) na one important
 ---
 ## AI, ML, Deep Learning
 
-![AI, ML, deep learning, data science](../../../../translated_images/ai-ml-ds.537ea441b124ebf6.pcm.png)
+![AI, ML, deep learning, data science](../../../../translated_images/pcm/ai-ml-ds.537ea441b124ebf6.webp)
 
 > One diagram wey dey show di relationship between AI, ML, deep learning, and data science. Infographic by [Jen Looper](https://twitter.com/jenlooper) wey e take inspiration from [dis graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
diff --git a/translations/pcm/1-Introduction/2-history-of-ML/README.md b/translations/pcm/1-Introduction/2-history-of-ML/README.md
index 5e708211e..2d8e13b6a 100644
--- a/translations/pcm/1-Introduction/2-history-of-ML/README.md
+++ b/translations/pcm/1-Introduction/2-history-of-ML/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # History of machine learning
 
-![Summary of History of machine learning in a sketchnote](../../../../translated_images/ml-history.a1bdfd4ce1f464d9.pcm.png)
+![Summary of History of machine learning in a sketchnote](../../../../translated_images/pcm/ml-history.a1bdfd4ce1f464d9.webp)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
@@ -71,14 +71,14 @@ Research dey well funded by government agencies, progress dey for computation an
 
 * [Shakey di robot](https://wikipedia.org/wiki/Shakey_the_robot), wey fit move and decide how e go do tasks 'intelligently'.
 
-    ![Shakey, an intelligent robot](../../../../translated_images/shakey.4dc17819c447c05b.pcm.jpg)
+    ![Shakey, an intelligent robot](../../../../translated_images/pcm/shakey.4dc17819c447c05b.webp)
     > Shakey for 1972
 
 ---
 
 * Eliza, one early 'chatterbot', fit talk with people and act like one basic 'therapist'. You go learn more about Eliza for di NLP lessons.
 
-    ![Eliza, a bot](../../../../translated_images/eliza.84397454cda9559b.pcm.png)
+    ![Eliza, a bot](../../../../translated_images/pcm/eliza.84397454cda9559b.webp)
     > One version of Eliza, one chatbot
 
 ---
diff --git a/translations/pcm/1-Introduction/3-fairness/README.md b/translations/pcm/1-Introduction/3-fairness/README.md
index e7e6d2c60..5b649e950 100644
--- a/translations/pcm/1-Introduction/3-fairness/README.md
+++ b/translations/pcm/1-Introduction/3-fairness/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # How to Build Machine Learning Solutions Wey Get Responsible AI
 
-![Summary of responsible AI in Machine Learning in a sketchnote](../../../../translated_images/ml-fairness.ef296ebec6afc98a.pcm.png)
+![Summary of responsible AI in Machine Learning in a sketchnote](../../../../translated_images/pcm/ml-fairness.ef296ebec6afc98a.webp)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
@@ -51,10 +51,10 @@ AI systems suppose treat everybody fairly and no dey affect similar groups diffe
 - **Over- or under-representation**. When one group no dey seen for one profession, and any service wey dey promote that na harm.
 - **Stereotyping**. To connect one group with pre-assigned attributes. For example, language translation system between English and Turkish fit get mistake because of words wey dey stereotype gender.
 
-![translation to Turkish](../../../../translated_images/gender-bias-translate-en-tr.f185fd8822c2d437.pcm.png)
+![translation to Turkish](../../../../translated_images/pcm/gender-bias-translate-en-tr.f185fd8822c2d437.webp)
 > translation to Turkish
 
-![translation back to English](../../../../translated_images/gender-bias-translate-tr-en.4eee7e3cecb8c70e.pcm.png)
+![translation back to English](../../../../translated_images/pcm/gender-bias-translate-tr-en.4eee7e3cecb8c70e.webp)
 > translation back to English
 
 When we dey design and test AI systems, we need to make sure say AI dey fair and no dey programmed to make biased or discriminatory decisions, wey humans no suppose make. To make AI and machine learning fair na one big sociotechnical challenge.
@@ -97,7 +97,7 @@ AI systems suppose dey easy to understand. Transparency mean to explain how AI s
 
 The people wey dey design and use AI systems suppose take responsibility for how their systems dey work. Accountability dey very important for sensitive technologies like facial recognition. Recently, demand for facial recognition technology don dey grow, especially from law enforcement wey wan use am to find missing children. But these technologies fit make government use am to take away people freedom, like to dey monitor specific people every time. So, data scientists and organizations need to take responsibility for how their AI system dey affect people or society.
 
-[![Leading AI Researcher Warns of Mass Surveillance Through Facial Recognition](../../../../translated_images/accountability.41d8c0f4b85b6231.pcm.png)](https://www.youtube.com/watch?v=Wldt8P5V6D0 "Microsoft's Approach to Responsible AI")
+[![Leading AI Researcher Warns of Mass Surveillance Through Facial Recognition](../../../../translated_images/pcm/accountability.41d8c0f4b85b6231.webp)](https://www.youtube.com/watch?v=Wldt8P5V6D0 "Microsoft's Approach to Responsible AI")
 
 > 🎥 Click the image above for a video: Warnings of Mass Surveillance Through Facial Recognition
 
diff --git a/translations/pcm/1-Introduction/4-techniques-of-ML/README.md b/translations/pcm/1-Introduction/4-techniques-of-ML/README.md
index e18828a4b..4a9f9f77d 100644
--- a/translations/pcm/1-Introduction/4-techniques-of-ML/README.md
+++ b/translations/pcm/1-Introduction/4-techniques-of-ML/README.md
@@ -97,7 +97,7 @@ For machine learning, model fitting mean how accurate di model function dey as e
 
 🎓 **Underfitting** and **overfitting** na common problems wey dey reduce di quality of di model, as di model fit no fit well or e fit too fit. Dis one dey make di model predictions either too close or too far from di training data. Overfit model dey predict training data too well because e don sabi di details and noise of di data too much. Underfit model no dey accurate because e no fit analyze di training data or di data wey e never see well.
 
-![overfitting model](../../../../translated_images/overfitting.1c132d92bfd93cb6.pcm.png)
+![overfitting model](../../../../translated_images/pcm/overfitting.1c132d92bfd93cb6.webp)
 > Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 ## Parameter tuning
diff --git a/translations/pcm/1-Introduction/README.md b/translations/pcm/1-Introduction/README.md
index 014314f48..8c99f5eae 100644
--- a/translations/pcm/1-Introduction/README.md
+++ b/translations/pcm/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For dis section of di curriculum, you go sabi di basic idea wey dey behind machine learning, wetin e mean, and you go learn about di history and di techniques wey researchers dey use to work with am. Make we waka enter dis new world of ML together!
 
-![globe](../../../translated_images/globe.59f26379ceb40428.pcm.jpg)
+![globe](../../../translated_images/pcm/globe.59f26379ceb40428.webp)
 > Foto by <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> for <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Lessons
diff --git a/translations/pcm/2-Regression/1-Tools/README.md b/translations/pcm/2-Regression/1-Tools/README.md
index c1e3f1ec6..069939eb7 100644
--- a/translations/pcm/2-Regression/1-Tools/README.md
+++ b/translations/pcm/2-Regression/1-Tools/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Start wit Python and Scikit-learn for regression models
 
-![Summary of regressions in a sketchnote](../../../../translated_images/ml-regression.4e4f70e3b3ed446e.pcm.png)
+![Summary of regressions in a sketchnote](../../../../translated_images/pcm/ml-regression.4e4f70e3b3ed446e.webp)
 
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
@@ -81,7 +81,7 @@ For dis folder, you go see di file _notebook.ipynb_.
     hello notebook
     ```
 
-![VS Code wit notebook open](../../../../translated_images/notebook.4a3ee31f396b8832.pcm.jpg)
+![VS Code wit notebook open](../../../../translated_images/pcm/notebook.4a3ee31f396b8832.webp)
 
 You fit mix your code wit comments to self-document di notebook.
 
@@ -211,7 +211,7 @@ For new code cell, load di diabetes dataset by calling `load_diabetes()`. Di inp
     plt.show()
     ```
 
-   ![scatterplot wey dey show datapoints about diabetes](../../../../translated_images/scatterplot.ad8b356bcbb33be6.pcm.png)
+   ![scatterplot wey dey show datapoints about diabetes](../../../../translated_images/pcm/scatterplot.ad8b356bcbb33be6.webp)
 ✅ Tink small small about wetin dey happen here. One straight line dey waka pass plenty small dots wey be data, but wetin e dey really do? You fit see how you go fit use dis line take predict where new data wey you never see go fit enter for the plot y axis? Try talk the practical use of dis model.
 
 Congrats, you don build your first linear regression model, use am predict something, and show am for one plot!
diff --git a/translations/pcm/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/pcm/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index c2f76e40f..1f20bd75a 100644
--- a/translations/pcm/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/pcm/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -46,7 +46,7 @@
     "So, make we start dis task!\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/encouRage.e75d5fe0367fb913.pcm.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/encouRage.e75d5fe0367fb913.webp\"\n",
     "   width=\"630\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
diff --git a/translations/pcm/2-Regression/2-Data/README.md b/translations/pcm/2-Regression/2-Data/README.md
index 225499c90..a73a020f6 100644
--- a/translations/pcm/2-Regression/2-Data/README.md
+++ b/translations/pcm/2-Regression/2-Data/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Build regression model wit Scikit-learn: prepare and show data
 
-![Data visualization infographic](../../../../translated_images/data-visualization.54e56dded7c1a804.pcm.png)
+![Data visualization infographic](../../../../translated_images/pcm/data-visualization.54e56dded7c1a804.webp)
 
 Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
@@ -183,7 +183,7 @@ Try create some basic plots to show di new dataframe wey you don create. Wetin b
     plt.show()
     ```
 
-    ![A scatterplot showing price to month relationship](../../../../translated_images/scatterplot.b6868f44cbd2051c.pcm.png)
+    ![A scatterplot showing price to month relationship](../../../../translated_images/pcm/scatterplot.b6868f44cbd2051c.webp)
 
     Dis plot dey useful? E surprise you?
 
@@ -200,7 +200,7 @@ To get charts wey dey useful, you go need group di data somehow. Make we try cre
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![A bar chart showing price to month relationship](../../../../translated_images/barchart.a833ea9194346d76.pcm.png)
+    ![A bar chart showing price to month relationship](../../../../translated_images/pcm/barchart.a833ea9194346d76.webp)
 
     Dis na more useful data visualization! E dey show say di highest price for pumpkins dey September and October. E match wetin you dey expect? Why or why not?
 
diff --git a/translations/pcm/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/pcm/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index a0775ed6d..fefbb6464 100644
--- a/translations/pcm/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/pcm/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -44,7 +44,7 @@
     "Make we see how e go be by working through one practical exercise.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/unruly_data.0eedc7ced92d2d91.pcm.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/unruly_data.0eedc7ced92d2d91.webp\"\n",
     "   width=\"700\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
@@ -222,12 +222,12 @@
     "## 3. Dplyr: Grammar wey dey for Data Manipulation\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/dplyr_wrangling.f5f99c64fd4580f1.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/dplyr_wrangling.f5f99c64fd4580f1.webp\"\n",
     "   width=\"569\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Artwork by \\@allison_horst](../../../../../../translated_images/dplyr_wrangling.f5f99c64fd4580f1.pcm.png)<br/>Artwork by \\@allison_horst-->\n"
+    "<!--![Artwork by \\@allison_horst](../../../../../../translated_images/pcm/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>Artwork by \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "o4jLY5-VZO2C"
@@ -525,12 +525,12 @@
     "## 4. Data visualization wit ggplot2\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/data-visualization.54e56dded7c1a804.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/data-visualization.54e56dded7c1a804.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infographic by Dasani Madipalli](../../../../../../translated_images/data-visualization.54e56dded7c1a804.pcm.png){width=\"600\"}-->\n",
+    "<!--![Infographic by Dasani Madipalli](../../../../../../translated_images/pcm/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "Dem get one *wise* saying wey talk like dis:\n",
     "\n",
diff --git a/translations/pcm/2-Regression/3-Linear/README.md b/translations/pcm/2-Regression/3-Linear/README.md
index a5d573d15..9c4f27402 100644
--- a/translations/pcm/2-Regression/3-Linear/README.md
+++ b/translations/pcm/2-Regression/3-Linear/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Build regression model wit Scikit-learn: regression four ways
 
-![Linear vs polynomial regression infographic](../../../../translated_images/linear-polynomial.5523c7cb6576ccab.pcm.png)
+![Linear vs polynomial regression infographic](../../../../translated_images/pcm/linear-polynomial.5523c7cb6576ccab.webp)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
 
@@ -66,13 +66,13 @@ We dey do am because we want model line wey get di least total distance from all
 >
 > `X` na di 'explanatory variable'. `Y` na di 'dependent variable'. Di slope of di line na `b` and `a` na di y-intercept, wey mean di value of `Y` when `X = 0`. 
 >
->![calculate di slope](../../../../translated_images/slope.f3c9d5910ddbfcf9.pcm.png)
+>![calculate di slope](../../../../translated_images/pcm/slope.f3c9d5910ddbfcf9.webp)
 >
 > First, calculate di slope `b`. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 >
 > In other words, and referring to our pumpkin data original question: "predict di price of pumpkin per bushel by month", `X` go mean di price and `Y` go mean di month of sale. 
 >
->![complete di equation](../../../../translated_images/calculation.a209813050a1ddb1.pcm.png)
+>![complete di equation](../../../../translated_images/pcm/calculation.a209813050a1ddb1.webp)
 >
 > Calculate di value of Y. If you dey pay around $4, e fit be April! Infographic by [Jen Looper](https://twitter.com/jenlooper)
 >
@@ -114,11 +114,11 @@ Now wey you don understand di math wey dey behind linear regression, make we cre
 
 From di previous lesson, you don probably see say di average price for different months dey look like dis:
 
-<img alt="Average price by month" src="../../../../translated_images/barchart.a833ea9194346d76.pcm.png" width="50%"/>
+<img alt="Average price by month" src="../../../../translated_images/pcm/barchart.a833ea9194346d76.webp" width="50%"/>
 
 Dis suggest say correlation dey, and we fit try train linear regression model to predict di relationship between `Month` and `Price`, or between `DayOfYear` and `Price`. Dis scatter plot dey show di relationship between `DayOfYear` and `Price`:
 
-<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/scatter-dayofyear.bc171c189c9fd553.pcm.png" width="50%" /> 
+<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/pcm/scatter-dayofyear.bc171c189c9fd553.webp" width="50%" /> 
 
 Make we see if correlation dey using di `corr` function:
 
@@ -137,7 +137,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()):
     ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)
 ```
 
-<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/scatter-dayofyear-color.65790faefbb9d54f.pcm.png" width="50%" /> 
+<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/pcm/scatter-dayofyear-color.65790faefbb9d54f.webp" width="50%" /> 
 
 Our investigation dey suggest say variety dey affect di overall price pass di actual selling date. We fit see dis wit bar graph:
 
@@ -145,7 +145,7 @@ Our investigation dey suggest say variety dey affect di overall price pass di ac
 new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')
 ```
 
-<img alt="Bar graph of price vs variety" src="../../../../translated_images/price-by-variety.744a2f9925d9bcb4.pcm.png" width="50%" /> 
+<img alt="Bar graph of price vs variety" src="../../../../translated_images/pcm/price-by-variety.744a2f9925d9bcb4.webp" width="50%" /> 
 
 Make we focus for now only on one pumpkin variety, di 'pie type', and see wetin di date dey do to di price:
 
@@ -153,7 +153,7 @@ Make we focus for now only on one pumpkin variety, di 'pie type', and see wetin
 pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']
 pie_pumpkins.plot.scatter('DayOfYear','Price') 
 ```
-<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/pie-pumpkins-scatter.d14f9804a53f927e.pcm.png" width="50%" /> 
+<img alt="Scatter plot of Price vs. Day of Year" src="../../../../translated_images/pcm/pie-pumpkins-scatter.d14f9804a53f927e.webp" width="50%" /> 
 
 If we now calculate di correlation between `Price` and `DayOfYear` using `corr` function, we go get something like `-0.27` - wey mean say training predictive model dey make sense.
 
@@ -228,7 +228,7 @@ plt.scatter(X_test,y_test)
 plt.plot(X_test,pred)
 ```
 
-<img alt="Linear regression" src="../../../../translated_images/linear-results.f7c3552c85b0ed1c.pcm.png" width="50%" />
+<img alt="Linear regression" src="../../../../translated_images/pcm/linear-results.f7c3552c85b0ed1c.webp" width="50%" />
 
 ## Polynomial Regression
 
@@ -257,7 +257,7 @@ Using `PolynomialFeatures(2)` mean say we go include all second-degree polynomia
 
 Pipelines fit dey used same way as the original `LinearRegression` object, like we fit `fit` the pipeline, then use `predict` to get the prediction results. Here be the graph wey show test data, and the approximation curve:
 
-<img alt="Polynomial regression" src="../../../../translated_images/poly-results.ee587348f0f1f60b.pcm.png" width="50%" />
+<img alt="Polynomial regression" src="../../../../translated_images/pcm/poly-results.ee587348f0f1f60b.webp" width="50%" />
 
 Using Polynomial Regression, we fit get slightly lower MSE and higher determination, but e no dey significant. We need to consider other features!
 
@@ -275,7 +275,7 @@ For ideal world, we go want predict prices for different pumpkin varieties using
 
 Here you fit see how average price dey depend on variety:
 
-<img alt="Average price by variety" src="../../../../translated_images/price-by-variety.744a2f9925d9bcb4.pcm.png" width="50%" />
+<img alt="Average price by variety" src="../../../../translated_images/pcm/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
 
 To take variety into account, we first need to change am to numeric form, or **encode** am. E get different ways we fit do am:
 
diff --git a/translations/pcm/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/pcm/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 10a7fb953..0c87f7fef 100644
--- a/translations/pcm/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/pcm/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -37,7 +37,7 @@
    "source": [
     "## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/linear-polynomial.5523c7cb6576ccab.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
@@ -159,7 +159,7 @@
     "## **2. Dance wit data: how we go take create data frame wey we go use for modelling**\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/janitor.e4a77dd3d3e6a32e.pcm.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/janitor.e4a77dd3d3e6a32e.webp\"\n",
     "   width=\"700\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
@@ -450,7 +450,7 @@
     "To do dat, you go need two more verbs: `prep()` and `bake()`, and as usual, our small R friends wey [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) don create go help you understand am better!\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/recipes.9ad10d8a4056bf89.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/recipes.9ad10d8a4056bf89.webp\"\n",
     "   width=\"550\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
@@ -561,7 +561,7 @@
     "## 4. Build linear regression model\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/linear-polynomial.5523c7cb6576ccab.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n"
    ],
@@ -799,12 +799,12 @@
     "## 5. Build polynomial regression model\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/linear-polynomial.5523c7cb6576ccab.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infographic by Dasani Madipalli](../../../../../../translated_images/linear-polynomial.5523c7cb6576ccab.pcm.png){width=\"800\"}-->\n"
+    "<!--![Infographic by Dasani Madipalli](../../../../../../translated_images/pcm/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "HOCqJXLTwtWI"
diff --git a/translations/pcm/2-Regression/4-Logistic/README.md b/translations/pcm/2-Regression/4-Logistic/README.md
index 4d59ec0dd..f8144ed9c 100644
--- a/translations/pcm/2-Regression/4-Logistic/README.md
+++ b/translations/pcm/2-Regression/4-Logistic/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Logistic regression to predict categories
 
-![Logistic vs. linear regression infographic](../../../../translated_images/linear-vs-logistic.ba180bf95e7ee667.pcm.png)
+![Logistic vs. linear regression infographic](../../../../translated_images/pcm/linear-vs-logistic.ba180bf95e7ee667.webp)
 
 ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
 
@@ -52,7 +52,7 @@ Logistic regression different from linear regression wey you don learn before, f
 
 Logistic regression no dey offer di same features as linear regression. Di former dey predict binary category ("white or not white") while di latter fit predict continuous values, like di price wey pumpkin go rise based on origin and harvest time.
 
-![Pumpkin classification Model](../../../../translated_images/pumpkin-classifier.562771f104ad5436.pcm.png)
+![Pumpkin classification Model](../../../../translated_images/pcm/pumpkin-classifier.562771f104ad5436.webp)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### Other classifications
@@ -62,7 +62,7 @@ Other types of logistic regression dey, like multinomial and ordinal:
 - **Multinomial**, wey involve more than one category - "Orange, White, and Striped".
 - **Ordinal**, wey involve ordered categories, useful if we wan arrange outcomes logically, like di pumpkin sizes (mini, sm, med, lg, xl, xxl).
 
-![Multinomial vs ordinal regression](../../../../translated_images/multinomial-vs-ordinal.36701b4850e37d86.pcm.png)
+![Multinomial vs ordinal regression](../../../../translated_images/pcm/multinomial-vs-ordinal.36701b4850e37d86.webp)
 
 ### Variables NO need to correlate
 
@@ -120,7 +120,7 @@ Seaborn get better ways to visualize your data. For example, you fit compare di
     )
     ```
 
-    ![A grid of visualized data](../../../../translated_images/pumpkins_catplot_1.c55c409b71fea2ec.pcm.png)
+    ![A grid of visualized data](../../../../translated_images/pcm/pumpkins_catplot_1.c55c409b71fea2ec.webp)
 
     By looking di data, you go see how di Color data relate to Variety.
 
@@ -199,7 +199,7 @@ Di best way to do dis analysis na to plot di data. We go use Seaborn `catplot` f
     g.set(xlabel="Item Size", ylabel="").set(xlim=(0,6))
     g.set_titles(row_template="{row_name}")
 ```
-![A catplot of visualized data](../../../../translated_images/pumpkins_catplot_2.87a354447880b388.pcm.png)
+![A catplot of visualized data](../../../../translated_images/pcm/pumpkins_catplot_2.87a354447880b388.webp)
 
 ### Use swarm plot
 
@@ -217,7 +217,7 @@ You fit visualize variables side-by-side with Seaborn plots.
     sns.swarmplot(x="Color", y="ord__Item Size", data=encoded_pumpkins, palette=palette)
     ```
 
-    ![A swarm of visualized data](../../../../translated_images/swarm_2.efeacfca536c2b57.pcm.png)
+    ![A swarm of visualized data](../../../../translated_images/pcm/swarm_2.efeacfca536c2b57.webp)
 
 **Watch Out**: di code above fit show warning, as seaborn fit struggle to represent plenty datapoints for swarm plot. One solution na to reduce di size of di marker, by using 'size' parameter. But make you sabi say e go affect how clear di plot go be.
 
@@ -225,7 +225,7 @@ You fit visualize variables side-by-side with Seaborn plots.
 >
 > Logistic regression dey use di concept of 'maximum likelihood' with [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). 'Sigmoid Function' for plot dey look like 'S' shape. E dey take value and map am to somewhere between 0 and 1. Di curve dey also called 'logistic curve'. Di formula be like dis:
 >
-> ![logistic function](../../../../translated_images/sigmoid.8b7ba9d095c789cf.pcm.png)
+> ![logistic function](../../../../translated_images/pcm/sigmoid.8b7ba9d095c789cf.webp)
 >
 > where di sigmoid midpoint dey for x's 0 point, L na di curve maximum value, and k na di curve steepness. If di function outcome pass 0.5, di label go dey class '1' for di binary choice. If e no pass, e go dey class '0'.
 
@@ -374,7 +374,7 @@ plt.show()
 
 Using Matplotlib, plot di model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves dey usually dey used to see di output of classifier in terms of di true vs. false positives. "ROC curves dey usually show true positive rate for di Y axis, and false positive rate for di X axis." So, di steepness of di curve and di space wey dey between di midpoint line and di curve matter: you go wan curve wey go quick go up and pass di line. For our case, we get false positives for di beginning, and di line later go up and pass di line well:
 
-![ROC](../../../../translated_images/ROC_2.777f20cdfc4988ca.pcm.png)
+![ROC](../../../../translated_images/pcm/ROC_2.777f20cdfc4988ca.webp)
 
 Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to calculate di actual 'Area Under di Curve' (AUC):
 
diff --git a/translations/pcm/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/pcm/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index abfb9883e..fad302c0b 100644
--- a/translations/pcm/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/pcm/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## Build logistic regression model - Lesson 4\n",
     "\n",
-    "![Logistic vs. linear regression infographic](../../../../../../translated_images/linear-vs-logistic.ba180bf95e7ee667.pcm.png)\n",
+    "![Logistic vs. linear regression infographic](../../../../../../translated_images/pcm/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "Logistic regression no dey do di same tin as linear regression. Logistic regression dey predict `binary category` (\"orange or not orange\") but linear regression fit predict `continual values`, like if you know where pumpkin come from and di time dem harvest am, *how much di price go increase*.\n",
     "\n",
-    "![Infographic by Dasani Madipalli](../../../../../../translated_images/pumpkin-classifier.562771f104ad5436.pcm.png)\n",
+    "![Infographic by Dasani Madipalli](../../../../../../translated_images/pcm/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### Other classifications\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **Ordinal**, wey mean say e get ordered categories, useful if we wan arrange di outcomes logically, like di pumpkins wey we fit arrange by size (mini, sm, med, lg, xl, xxl).\n",
     "\n",
-    "![Multinomial vs ordinal regression](../../../../../../translated_images/multinomial-vs-ordinal.36701b4850e37d86.pcm.png)\n",
+    "![Multinomial vs ordinal regression](../../../../../../translated_images/pcm/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **Variables NO need to correlate**\n",
     "\n",
diff --git a/translations/pcm/2-Regression/README.md b/translations/pcm/2-Regression/README.md
index 62c9c3a98..254bab935 100644
--- a/translations/pcm/2-Regression/README.md
+++ b/translations/pcm/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For North America, dem dey use pumpkin plenty for Halloween, dem dey carve am make e look scary. Make we learn more about dis vegetable wey dey interesting!
 
-![jack-o-lanterns](../../../translated_images/jack-o-lanterns.181c661a9212457d.pcm.jpg)
+![jack-o-lanterns](../../../translated_images/pcm/jack-o-lanterns.181c661a9212457d.webp)
 > Foto by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> for <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Wetin you go learn
diff --git a/translations/pcm/3-Web-App/README.md b/translations/pcm/3-Web-App/README.md
index 83e1929e4..c488d0de5 100644
--- a/translations/pcm/3-Web-App/README.md
+++ b/translations/pcm/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For dis part of di curriculum, dem go show you one practical ML topic: how you go fit save your Scikit-learn model as file wey you fit use to make predictions inside web application. Once you don save di model, you go learn how to use am for web app wey dem build with Flask. First, you go create one model using some data wey dey about UFO sightings! After dat, you go build one web app wey go allow you put di number of seconds, latitude, and longitude value to predict which country report say dem see UFO.
 
-![UFO Parking](../../../translated_images/ufo.9e787f5161da9d4d.pcm.jpg)
+![UFO Parking](../../../translated_images/pcm/ufo.9e787f5161da9d4d.webp)
 
 Photo by <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> on <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
diff --git a/translations/pcm/4-Classification/1-Introduction/README.md b/translations/pcm/4-Classification/1-Introduction/README.md
index e706318b2..2e65231ed 100644
--- a/translations/pcm/4-Classification/1-Introduction/README.md
+++ b/translations/pcm/4-Classification/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For dis four lessons, you go learn one important part of classic machine learning - _classification_. We go show you how to use different classification algorithms with one dataset wey dey talk about all di sweet cuisines for Asia and India. Hope say you dey hungry!
 
-![just a pinch!](../../../../translated_images/pinch.1b035ec9ba7e0d40.pcm.png)
+![just a pinch!](../../../../translated_images/pcm/pinch.1b035ec9ba7e0d40.webp)
 
 > Make we celebrate pan-Asian cuisines for dis lessons! Image by [Jen Looper](https://twitter.com/jenlooper)
 
@@ -38,7 +38,7 @@ Classification na one of di main work wey machine learning researchers and data
 
 To talk di process in one more scientific way, di classification method go create one predictive model wey go help you map di relationship between input variables to output variables.
 
-![binary vs. multiclass classification](../../../../translated_images/binary-multiclass.b56d0c86c81105a6.pcm.png)
+![binary vs. multiclass classification](../../../../translated_images/pcm/binary-multiclass.b56d0c86c81105a6.webp)
 
 > Binary vs. multiclass problems wey classification algorithms dey handle. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
@@ -134,7 +134,7 @@ Now di work dey start to dey interesting. Make we find di distribution of data,
     df.cuisine.value_counts().plot.barh()
     ```
 
-    ![cuisine data distribution](../../../../translated_images/cuisine-dist.d0cc2d551abe5c25.pcm.png)
+    ![cuisine data distribution](../../../../translated_images/pcm/cuisine-dist.d0cc2d551abe5c25.webp)
 
     We get limited number of cuisines, but di distribution of data no dey even. You fit fix am! Before you do am, explore small more.
 
@@ -188,7 +188,7 @@ Now you fit dig deeper into di data and learn wetin be di common ingredients for
     thai_ingredient_df.head(10).plot.barh()
     ```
 
-    ![thai](../../../../translated_images/thai.0269dbab2e78bd38.pcm.png)
+    ![thai](../../../../translated_images/pcm/thai.0269dbab2e78bd38.webp)
 
 1. Do di same for di Japanese data:
 
@@ -197,7 +197,7 @@ Now you fit dig deeper into di data and learn wetin be di common ingredients for
     japanese_ingredient_df.head(10).plot.barh()
     ```
 
-    ![japanese](../../../../translated_images/japanese.30260486f2a05c46.pcm.png)
+    ![japanese](../../../../translated_images/pcm/japanese.30260486f2a05c46.webp)
 
 1. Now for di Chinese ingredients:
 
@@ -206,7 +206,7 @@ Now you fit dig deeper into di data and learn wetin be di common ingredients for
     chinese_ingredient_df.head(10).plot.barh()
     ```
 
-    ![chinese](../../../../translated_images/chinese.e62cafa5309f111a.pcm.png)
+    ![chinese](../../../../translated_images/pcm/chinese.e62cafa5309f111a.webp)
 
 1. Plot di Indian ingredients:
 
@@ -215,7 +215,7 @@ Now you fit dig deeper into di data and learn wetin be di common ingredients for
     indian_ingredient_df.head(10).plot.barh()
     ```
 
-    ![indian](../../../../translated_images/indian.2c4292002af1a1f9.pcm.png)
+    ![indian](../../../../translated_images/pcm/indian.2c4292002af1a1f9.webp)
 
 1. Finally, plot di Korean ingredients:
 
@@ -224,7 +224,7 @@ Now you fit dig deeper into di data and learn wetin be di common ingredients for
     korean_ingredient_df.head(10).plot.barh()
     ```
 
-    ![korean](../../../../translated_images/korean.4a4f0274f3d9805a.pcm.png)
+    ![korean](../../../../translated_images/pcm/korean.4a4f0274f3d9805a.webp)
 
 1. Now, drop di common ingredients wey dey cause confusion between different cuisines, by calling `drop()`:
 
diff --git a/translations/pcm/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/pcm/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
index 7820d50dc..ad4e3dc30 100644
--- a/translations/pcm/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
+++ b/translations/pcm/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -39,7 +39,7 @@
     "For dis four lessons, you go learn one of di main tins wey classic machine learning dey focus on - *classification*. We go waka through how to use different classification algorithms with one dataset about all di sweet cuisines wey dey Asia and India. Hope say you don dey hungry!\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/pinch.1b035ec9ba7e0d40.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/pinch.1b035ec9ba7e0d40.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Celebrate pan-Asian cuisines for dis lessons! Image by Jen Looper</figcaption>\n",
     "\n",
@@ -62,7 +62,7 @@
     "To talk di process in one more scientific way, your classification method go create one predictive model wey go help you map di relationship between input variables and output variables.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/binary-multiclass.b56d0c86c81105a6.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/binary-multiclass.b56d0c86c81105a6.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Binary vs. multiclass problems wey classification algorithms dey handle. Infographic by Jen Looper</figcaption>\n",
     "\n",
@@ -222,7 +222,7 @@
     "> A [tibble](https://tibble.tidyverse.org/) na modern data frame.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/dplyr_filter.b480b264b03439ff.pcm.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/dplyr_filter.b480b264b03439ff.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
@@ -510,7 +510,7 @@
     "## How to take care of data wey no balance ⚖️ - Use recipes 👩‍🍳👨‍🍳\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/recipes.186acfa8ed2e8f00.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/recipes.186acfa8ed2e8f00.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n",
     "\n",
@@ -705,7 +705,7 @@
     "[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for di original Python version of dis module ♥️\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/r_learners_sm.cd14eb3581a9f28d.pcm.jpeg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/r_learners_sm.cd14eb3581a9f28d.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
diff --git a/translations/pcm/4-Classification/2-Classifiers-1/README.md b/translations/pcm/4-Classification/2-Classifiers-1/README.md
index 56e3bf396..f32cf7e80 100644
--- a/translations/pcm/4-Classification/2-Classifiers-1/README.md
+++ b/translations/pcm/4-Classification/2-Classifiers-1/README.md
@@ -107,7 +107,7 @@ Scikit-learn dey group classification under Supervised Learning, and for dat cat
 
 So, which classifier you go use? Sometimes, to try different ones and check di result na one way to test. Scikit-learn dey offer [side-by-side comparison](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) for one created dataset, wey compare KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, wey show di results visualized:
 
-![comparison of classifiers](../../../../translated_images/comparison.edfab56193a85e7f.pcm.png)
+![comparison of classifiers](../../../../translated_images/pcm/comparison.edfab56193a85e7f.webp)
 > Plots wey dem generate for Scikit-learn documentation
 
 > AutoML dey solve dis problem well by running dis comparisons for di cloud, e go allow you choose di best algorithm for your data. Try am [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)
@@ -116,7 +116,7 @@ So, which classifier you go use? Sometimes, to try different ones and check di r
 
 Better way wey pass to dey guess anyhow na to follow di ideas wey dey dis downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Here, we go see say for our multiclass problem, we get some options:
 
-![cheatsheet for multiclass problems](../../../../translated_images/cheatsheet.07a475ea444d2223.pcm.png)
+![cheatsheet for multiclass problems](../../../../translated_images/pcm/cheatsheet.07a475ea444d2223.webp)
 > Part of Microsoft's Algorithm Cheat Sheet, wey show multiclass classification options
 
 ✅ Download dis cheat sheet, print am, and hang am for your wall!
@@ -147,7 +147,7 @@ According to di docs, for di multiclass case, di training algorithm:
 
 Scikit-learn dey offer dis table to explain how solvers dey handle different challenges wey different kinds of data structures dey bring:
 
-![solvers](../../../../translated_images/solvers.5fc648618529e627.pcm.png)
+![solvers](../../../../translated_images/pcm/solvers.5fc648618529e627.webp)
 
 ## Exercise - split di data
 
diff --git a/translations/pcm/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/pcm/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
index 418fbb743..e4e03e662 100644
--- a/translations/pcm/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
+++ b/translations/pcm/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -564,7 +564,7 @@
     "## 3. Choose your classifier\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/parsnip.cd2ce92622976502.pcm.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/parsnip.cd2ce92622976502.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ],
@@ -618,7 +618,7 @@
     "One better way wey pass to dey guess anyhow na to follow di ideas wey dey for dis downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). For here, we go see say for our multiclass problem, we get some options:\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/cheatsheet.07a475ea444d2223.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/cheatsheet.07a475ea444d2223.webp\"\n",
     "   width=\"500\"/>\n",
     "   <figcaption>One part of Microsoft's Algorithm Cheat Sheet, wey dey show multiclass classification options</figcaption>\n"
    ],
diff --git a/translations/pcm/4-Classification/3-Classifiers-2/README.md b/translations/pcm/4-Classification/3-Classifiers-2/README.md
index 05ae3c8e3..68efa5586 100644
--- a/translations/pcm/4-Classification/3-Classifiers-2/README.md
+++ b/translations/pcm/4-Classification/3-Classifiers-2/README.md
@@ -25,7 +25,7 @@ We don load your _notebook.ipynb_ file with the clean dataset and we don divide
 
 Before, you don learn about the different options wey you fit use to classify data using Microsoft's cheat sheet. Scikit-learn get similar cheat sheet wey dey more detailed and fit help you narrow down your estimators (another name for classifiers):
 
-![ML Map from Scikit-learn](../../../../translated_images/map.e963a6a51349425a.pcm.png)
+![ML Map from Scikit-learn](../../../../translated_images/pcm/map.e963a6a51349425a.webp)
 > Tip: [visit dis map online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) and click along the path to read documentation.
 
 ### The plan
diff --git a/translations/pcm/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/pcm/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
index cd4cf314f..76f9e2302 100644
--- a/translations/pcm/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
+++ b/translations/pcm/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -90,7 +90,7 @@
     "Before, we don learn about di different options wey dey available to classify data using Microsoft's cheat sheet. Python Machine Learning framework, Scikit-learn, get one cheat sheet wey dey more detailed wey fit help you narrow down your estimators (another name for classifiers):\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/map.e963a6a51349425a.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/map.e963a6a51349425a.webp\"\n",
     "   width=\"700\"/>\n",
     "   <figcaption></figcaption>\n"
    ]
@@ -233,7 +233,7 @@
     "For classification matter, `Support Vector Machines` na one machine learning method wey dey try find one *hyperplane* wey go \"best\" separate di classes. Make we see one simple example:\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/svm.621ae7b516d678e0.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/svm.621ae7b516d678e0.webp\"\n",
     "   width=\"300\"/>\n",
     "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
    ]
@@ -638,7 +638,7 @@
     "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/r_learners_sm.f9199f76f1e2e493.pcm.jpeg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/r_learners_sm.f9199f76f1e2e493.webp\"\n",
     "   width=\"569\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ]
diff --git a/translations/pcm/4-Classification/4-Applied/README.md b/translations/pcm/4-Classification/4-Applied/README.md
index 6c30e16d8..7dbe9796a 100644
--- a/translations/pcm/4-Classification/4-Applied/README.md
+++ b/translations/pcm/4-Classification/4-Applied/README.md
@@ -152,7 +152,7 @@ If you run di whole notebook now, e go build Onnx model and save am for dis fold
 
 Onnx models no dey too visible for Visual Studio code, but one free software wey researchers dey use to see di model dey available. Download [Netron](https://github.com/lutzroeder/Netron) and open your model.onnx file. You go see your simple model visualized, with di 380 inputs and classifier listed:
 
-![Netron visual](../../../../translated_images/netron.a05f39410211915e.pcm.png)
+![Netron visual](../../../../translated_images/pcm/netron.a05f39410211915e.webp)
 
 Netron na helpful tool to view your models.
 
@@ -301,7 +301,7 @@ For dis code, plenty things dey happen:
 
 Open terminal session for Visual Studio Code for di folder wey your index.html file dey. Make sure say you don install [http-server](https://www.npmjs.com/package/http-server) globally, and type `http-server` for di prompt. Localhost go open and you fit view your web app. Check wetin di app recommend based on di ingredients wey you select:
 
-![ingredient web app](../../../../translated_images/web-app.4c76450cabe20036.pcm.png)
+![ingredient web app](../../../../translated_images/pcm/web-app.4c76450cabe20036.webp)
 
 Congrats, you don create 'recommendation' web app wey get small fields. Take time to build dis system well!
 
diff --git a/translations/pcm/4-Classification/README.md b/translations/pcm/4-Classification/README.md
index 2ddbe1415..5675f87e7 100644
--- a/translations/pcm/4-Classification/README.md
+++ b/translations/pcm/4-Classification/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For Asia and India, food tradition dem plenty well well, and dem sweet no be small! Make we check data about food for dis region to sabi di kain ingredients wey dem dey use.
 
-![Thai food seller](../../../translated_images/thai-food.c47a7a7f9f05c218.pcm.jpg)
+![Thai food seller](../../../translated_images/pcm/thai-food.c47a7a7f9f05c218.webp)
 > Foto by <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> for <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Wetin you go learn
diff --git a/translations/pcm/5-Clustering/1-Visualize/README.md b/translations/pcm/5-Clustering/1-Visualize/README.md
index b3ec25ae2..3ddb18d4a 100644
--- a/translations/pcm/5-Clustering/1-Visualize/README.md
+++ b/translations/pcm/5-Clustering/1-Visualize/README.md
@@ -70,7 +70,7 @@ Make you learn more about clustering techniques for dis [Learn module](https://d
 >
 >'Flat' for dis context mean Euclidean geometry (parts of am dey taught as 'plane' geometry), and non-flat mean non-Euclidean geometry. Wetin geometry get to do with machine learning? Well, as di two fields dey based on mathematics, we need common way to measure distance between points for clusters, and we fit do am in 'flat' or 'non-flat' way, depending on di nature of di data. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance) dey measure di length of line segment between two points. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry) dey measure distance along curve. If your data, when you visualize am, no dey for plane, you go need special algorithm to handle am.
 >
-![Flat vs Nonflat Geometry Infographic](../../../../translated_images/flat-nonflat.d1c8c6e2a96110c1.pcm.png)
+![Flat vs Nonflat Geometry Infographic](../../../../translated_images/pcm/flat-nonflat.d1c8c6e2a96110c1.webp)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 > 
 > 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
@@ -93,12 +93,12 @@ Plenty clustering algorithms dey, more than 100, and di one wey you go use depen
 
 - **Hierarchical clustering**. If object dey classified by how e near another object, instead of how far e dey, clusters go form based on di distance of di members to and from other objects. Scikit-learn agglomerative clustering na hierarchical.
 
-   ![Hierarchical clustering Infographic](../../../../translated_images/hierarchical.bf59403aa43c8c47.pcm.png)
+   ![Hierarchical clustering Infographic](../../../../translated_images/pcm/hierarchical.bf59403aa43c8c47.webp)
    > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 - **Centroid clustering**. Dis popular algorithm dey require make you choose 'k', or di number of clusters wey you wan form, then di algorithm go find di center point of di cluster and gather data around di point. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) na popular version of centroid clustering. Di center dey determined by di nearest mean, na why dem call am di name. Di squared distance from di cluster dey minimized.
 
-   ![Centroid clustering Infographic](../../../../translated_images/centroid.097fde836cf6c918.pcm.png)
+   ![Centroid clustering Infographic](../../../../translated_images/pcm/centroid.097fde836cf6c918.webp)
    > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 - **Distribution-based clustering**. Dis one dey based on statistical modeling, e dey focus on di probability say data point belong to cluster, then e go assign am. Gaussian mixture methods dey belong to dis type.
@@ -234,7 +234,7 @@ Make we look di general values for di data. Note say popularity fit be '0', wey
     plt.title('Top genres',color = 'blue')
     ```
 
-    ![most popular](../../../../translated_images/popular.9c48d84b3386705f.pcm.png)
+    ![most popular](../../../../translated_images/pcm/popular.9c48d84b3386705f.webp)
 
 ✅ If you wan see more top values, change di top `[:5]` to bigger value, or remove am to see all.
 
@@ -253,7 +253,7 @@ Note, when di top genre dey described as 'Missing', e mean say Spotify no classi
 
     Now check di genres again:
 
-    ![most popular](../../../../translated_images/all-genres.1d56ef06cefbfcd6.pcm.png)
+    ![most popular](../../../../translated_images/pcm/all-genres.1d56ef06cefbfcd6.webp)
 
 1. Di top three genres dey dominate dis dataset. Make we focus on `afro dancehall`, `afropop`, and `nigerian pop`, plus filter di dataset to remove anything wey get 0 popularity value (meaning e no dey classified with popularity for di dataset and fit be noise for our purpose):
 
@@ -275,7 +275,7 @@ Note, when di top genre dey described as 'Missing', e mean say Spotify no classi
     sns.heatmap(corrmat, vmax=.8, square=True)
     ```
 
-    ![correlations](../../../../translated_images/correlation.a9356bb798f5eea5.pcm.png)
+    ![correlations](../../../../translated_images/pcm/correlation.a9356bb798f5eea5.webp)
 
     Di only strong correlation na between `energy` and `loudness`, wey no too surprise, as loud music dey usually energetic. Otherwise, di correlations dey relatively weak. E go dey interesting to see wetin clustering algorithm fit do with dis data.
 
@@ -307,7 +307,7 @@ Di three genres dey different well well for di perception of their danceability,
 
     Generally, di three genres dey align small in terms of their popularity and danceability. To find clusters for dis loosely-aligned data go dey challenging:
 
-    ![distribution](../../../../translated_images/distribution.9be11df42356ca95.pcm.png)
+    ![distribution](../../../../translated_images/pcm/distribution.9be11df42356ca95.webp)
 
 1. Create scatter plot:
 
@@ -319,7 +319,7 @@ Di three genres dey different well well for di perception of their danceability,
 
     Scatterplot for di same axes dey show similar pattern of convergence
 
-    ![Facetgrid](../../../../translated_images/facetgrid.9b2e65ce707eba1f.pcm.png)
+    ![Facetgrid](../../../../translated_images/pcm/facetgrid.9b2e65ce707eba1f.webp)
 
 Generally, for clustering, you fit use scatterplots to show clusters of data, so e good to sabi dis type of visualization well. For di next lesson, we go use dis filtered data and use k-means clustering to find groups for dis data wey dey overlap in interesting ways.
 
diff --git a/translations/pcm/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/pcm/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
index 687850c2f..d52b4a434 100644
--- a/translations/pcm/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
+++ b/translations/pcm/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -42,7 +42,7 @@
     "> 'Flat' for dis context na Euclidean geometry (di one wey dem dey teach as 'plane' geometry), and non-flat na non-Euclidean geometry. Wetin geometry get to do with machine learning? Well, since di two na mathematics, dem need common way to measure distance between points for clusters, and e fit be 'flat' or 'non-flat' way, depending on di data. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance) dey measure di length of line between two points. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry) dey measure distance along curve. If your data no dey for plane when you visualize am, you go need special algorithm to handle am.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/flat-nonflat.d1c8c6e2a96110c1.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/flat-nonflat.d1c8c6e2a96110c1.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
@@ -71,7 +71,7 @@
     "-   **Hierarchical clustering**. If object dey classified by how e near another object, instead of di one wey far, clusters go form based on di distance between di members. Hierarchical clustering dey combine two clusters repeatedly.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/hierarchical.bf59403aa43c8c47.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/hierarchical.bf59403aa43c8c47.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
@@ -80,7 +80,7 @@
     "-   **Centroid clustering**. Dis popular algorithm dey require make you choose 'k', di number of clusters wey you wan form, then di algorithm go find di center point of di cluster and gather data around di point. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) na popular version of centroid clustering wey dey divide dataset into K groups wey you don define. Di center dey determined by di nearest mean, na why dem call am so. Di squared distance from di cluster dey minimized.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/centroid.097fde836cf6c918.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/centroid.097fde836cf6c918.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Infographic by Dasani Madipalli</figcaption>\n",
     "\n",
diff --git a/translations/pcm/5-Clustering/2-K-Means/README.md b/translations/pcm/5-Clustering/2-K-Means/README.md
index 920aee2eb..79058118c 100644
--- a/translations/pcm/5-Clustering/2-K-Means/README.md
+++ b/translations/pcm/5-Clustering/2-K-Means/README.md
@@ -26,7 +26,7 @@ Terms wey you go learn about:
 
 Di clusters fit dey visualized as [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram), wey include one point (or 'seed') and di region wey dey follow am.
 
-![voronoi diagram](../../../../translated_images/voronoi.1dc1613fb0439b95.pcm.png)
+![voronoi diagram](../../../../translated_images/pcm/voronoi.1dc1613fb0439b95.webp)
 
 > infographic by [Jen Looper](https://twitter.com/jenlooper)
 
@@ -91,7 +91,7 @@ Start by looking di songs data again.
 
     Dis data dey small noisy: if you observe each column as boxplot, you go see outliers.
 
-    ![outliers](../../../../translated_images/boxplots.8228c29dabd0f292.pcm.png)
+    ![outliers](../../../../translated_images/pcm/boxplots.8228c29dabd0f292.webp)
 
 You fit go through di dataset and remove di outliers, but e go make di data small.
 
@@ -187,7 +187,7 @@ Before, you don reason say, because you dey target 3 song genres, you suppose ch
 
     Use di `wcss` variable wey you build for di previous step to create one chart wey dey show where di 'bend' for di elbow dey, wey dey indicate di optimum number of clusters. Maybe e **dey** 3!
 
-    ![elbow method](../../../../translated_images/elbow.72676169eed744ff.pcm.png)
+    ![elbow method](../../../../translated_images/pcm/elbow.72676169eed744ff.webp)
 
 ## Exercise - display di clusters
 
@@ -218,13 +218,13 @@ Before, you don reason say, because you dey target 3 song genres, you suppose ch
 
     Dis model accuracy no too good, and di shape of di clusters dey give you hint why.
 
-    ![clusters](../../../../translated_images/clusters.b635354640d8e4fd.pcm.png)
+    ![clusters](../../../../translated_images/pcm/clusters.b635354640d8e4fd.webp)
 
     Dis data dey too imbalanced, e no too correlate and di variance between di column values too much to cluster well. In fact, di clusters wey form fit dey heavily influenced or skewed by di three genre categories wey we define above. Na learning process!
 
     For Scikit-learn documentation, you fit see say model like dis one, wit clusters wey no too demarcate well, get 'variance' problem:
 
-    ![problem models](../../../../translated_images/problems.f7fb539ccd80608e.pcm.png)
+    ![problem models](../../../../translated_images/pcm/problems.f7fb539ccd80608e.webp)
     > Infographic from Scikit-learn
 
 ## Variance
diff --git a/translations/pcm/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/pcm/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
index 6c3a5e323..6826428ac 100644
--- a/translations/pcm/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
+++ b/translations/pcm/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -59,7 +59,7 @@
     "Di clusters fit show as [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram), wey get one point (or 'seed') and di region wey dey follow am.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/voronoi.1dc1613fb0439b95.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/voronoi.1dc1613fb0439b95.webp\"\n",
     "   width=\"500\"/>\n",
     "   <figcaption>Infographic by Jen Looper</figcaption>\n",
     "\n",
@@ -573,7 +573,7 @@
     "For Scikit-learn documentation, you go see say model like dis one, wey di clusters no dey well demarcated, get 'variance' problem:\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/problems.f7fb539ccd80608e.pcm.png\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/problems.f7fb539ccd80608e.webp\"\n",
     "   width=\"500\"/>\n",
     "   <figcaption>Infographic from Scikit-learn</figcaption>\n",
     "\n",
@@ -626,7 +626,7 @@
     "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/r_learners_sm.e4a71b113ffbedfe.pcm.jpeg\"\n",
+    "   <img src=\"../../../../../../translated_images/pcm/r_learners_sm.e4a71b113ffbedfe.webp\"\n",
     "   width=\"500\"/>\n",
     "   <figcaption>Artwork by @allison_horst</figcaption>\n"
    ]
diff --git a/translations/pcm/5-Clustering/README.md b/translations/pcm/5-Clustering/README.md
index 69d5686b6..ab8dae6e4 100644
--- a/translations/pcm/5-Clustering/README.md
+++ b/translations/pcm/5-Clustering/README.md
@@ -15,7 +15,7 @@ Clustering na machine learning task wey dey try find objects wey resemble each o
 
 Nigeria get plenty different people and dem music taste dey different too. Using data wey dem collect from Spotify (inspired by [dis article](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), make we check some music wey dey popular for Nigeria. Dis dataset get data about different songs like 'danceability' score, 'acousticness', loudness, 'speechiness', popularity, and energy. E go dey interesting to see wetin we fit discover for dis data!
 
-![A turntable](../../../translated_images/turntable.f2b86b13c53302dc.pcm.jpg)
+![A turntable](../../../translated_images/pcm/turntable.f2b86b13c53302dc.webp)
 
 > Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/pcm/6-NLP/1-Introduction-to-NLP/README.md b/translations/pcm/6-NLP/1-Introduction-to-NLP/README.md
index fcb7f9340..70208d949 100644
--- a/translations/pcm/6-NLP/1-Introduction-to-NLP/README.md
+++ b/translations/pcm/6-NLP/1-Introduction-to-NLP/README.md
@@ -32,7 +32,7 @@ Computational linguistics na research and development area wey don dey for many
 
 If you don ever dictate for your phone instead of typing or ask virtual assistant question, di speech wey you talk go turn text and dem go process or *parse* di language wey you talk. Di keywords wey dem detect go then turn format wey di phone or assistant fit understand and act on.
 
-![comprehension](../../../../translated_images/comprehension.619708fc5959b0f6.pcm.png)
+![comprehension](../../../../translated_images/pcm/comprehension.619708fc5959b0f6.webp)
 > To really understand language no easy! Image by [Jen Looper](https://twitter.com/jenlooper)
 
 ### How dem take make dis technology possible?
diff --git a/translations/pcm/6-NLP/2-Tasks/README.md b/translations/pcm/6-NLP/2-Tasks/README.md
index 6dd854c0d..93d43b636 100644
--- a/translations/pcm/6-NLP/2-Tasks/README.md
+++ b/translations/pcm/6-NLP/2-Tasks/README.md
@@ -23,14 +23,14 @@ Plenty ways dey to analyze di text wey you dey work on. You fit do some tasks we
 
 Di first thing wey most NLP algorithms dey do na to divide di text into tokens, or words. E dey look simple, but di wahala of punctuation and di way different languages dey use word and sentence separator fit make am hard. You go need use different methods to know where e dey start and end.
 
-![tokenization](../../../../translated_images/tokenization.1641a160c66cd2d9.pcm.png)
+![tokenization](../../../../translated_images/pcm/tokenization.1641a160c66cd2d9.webp)
 > Tokenizing one sentence from **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 ### Embeddings
 
 [Word embeddings](https://wikipedia.org/wiki/Word_embedding) na one way to change your text data to numbers. Dem dey do embeddings so dat words wey get similar meaning or wey dey appear together go dey close to each other.
 
-![word embeddings](../../../../translated_images/embedding.2cf8953c4b3101d1.pcm.png)
+![word embeddings](../../../../translated_images/pcm/embedding.2cf8953c4b3101d1.webp)
 > "I have the highest respect for your nerves, they are my old friends." - Word embeddings for one sentence from **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 ✅ Try [dis tool wey dey interesting](https://projector.tensorflow.org/) to test word embeddings. If you click one word, e go show clusters of similar words: 'toy' dey cluster wit 'disney', 'lego', 'playstation', and 'console'.
@@ -39,7 +39,7 @@ Di first thing wey most NLP algorithms dey do na to divide di text into tokens,
 
 Every word wey dem don tokenize fit get tag as part of speech - like noun, verb, or adjective. Di sentence `the quick red fox jumped over the lazy brown dog` fit get POS tag like fox = noun, jumped = verb.
 
-![parsing](../../../../translated_images/parse.d0c5bbe1106eae8f.pcm.png)
+![parsing](../../../../translated_images/pcm/parse.d0c5bbe1106eae8f.webp)
 
 > Parsing one sentence from **Pride and Prejudice**. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
diff --git a/translations/pcm/6-NLP/3-Translation-Sentiment/README.md b/translations/pcm/6-NLP/3-Translation-Sentiment/README.md
index 5082df731..8f5437f27 100644
--- a/translations/pcm/6-NLP/3-Translation-Sentiment/README.md
+++ b/translations/pcm/6-NLP/3-Translation-Sentiment/README.md
@@ -56,7 +56,7 @@ For example, look di case of *Pride and Prejudice*, one popular English novel we
 
 For example, if English phrase like `I have no money` dey translate word for word to French, e fit turn `Je n'ai pas de monnaie`. "Monnaie" na tricky French 'false cognate', because 'money' and 'monnaie' no mean di same thing. Better translation wey human fit do na `Je n'ai pas d'argent`, because e go explain di meaning well say you no get money (no be 'loose change' wey be di meaning of 'monnaie').
 
-![monnaie](../../../../translated_images/monnaie.606c5fa8369d5c3b.pcm.png)
+![monnaie](../../../../translated_images/pcm/monnaie.606c5fa8369d5c3b.webp)
 
 > Image by [Jen Looper](https://twitter.com/jenlooper)
 
diff --git a/translations/pcm/6-NLP/README.md b/translations/pcm/6-NLP/README.md
index a8d4a42bd..d40c03c30 100644
--- a/translations/pcm/6-NLP/README.md
+++ b/translations/pcm/6-NLP/README.md
@@ -17,7 +17,7 @@ For dis part of di curriculum, dem go show you one of di most common ways wey ma
 
 For dis lessons, we go learn di basics of NLP by building small conversational bots to see how machine learning dey help make di conversations dey more 'smart'. You go travel go back in time, dey chat with Elizabeth Bennett and Mr. Darcy from Jane Austen's classic novel, **Pride and Prejudice**, wey dem publish for 1813. After dat, you go learn more by studying sentiment analysis through hotel reviews for Europe.
 
-![Pride and Prejudice book and tea](../../../translated_images/p&p.279f1c49ecd88941.pcm.jpg)
+![Pride and Prejudice book and tea](../../../translated_images/pcm/p&p.279f1c49ecd88941.webp)
 > Photo by <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> on <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Lessons
diff --git a/translations/pcm/7-TimeSeries/1-Introduction/README.md b/translations/pcm/7-TimeSeries/1-Introduction/README.md
index 11f0fd192..1d844a588 100644
--- a/translations/pcm/7-TimeSeries/1-Introduction/README.md
+++ b/translations/pcm/7-TimeSeries/1-Introduction/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Introduction to time series forecasting
 
-![Summary of time series in a sketchnote](../../../../translated_images/ml-timeseries.fb98d25f1013fc0c.pcm.png)
+![Summary of time series in a sketchnote](../../../../translated_images/pcm/ml-timeseries.fb98d25f1013fc0c.webp)
 
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
@@ -112,7 +112,7 @@ Di data fit show abrupt change wey need more analysis. Example na di sudden clos
 
 ✅ Dis [sample time series plot](https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python) dey show daily in-game currency wey people dey spend over some years. You fit identify any of di characteristics wey we list for dis data?
 
-![In-game currency spend](../../../../translated_images/currency.e7429812bfc8c608.pcm.png)
+![In-game currency spend](../../../../translated_images/pcm/currency.e7429812bfc8c608.webp)
 
 ## Exercise - getting started with power usage data
 
@@ -160,7 +160,7 @@ Make we start to create time series model to predict future power usage based on
     plt.show()
     ```
 
-    ![energy plot](../../../../translated_images/energy-plot.5fdac3f397a910bc.pcm.png)
+    ![energy plot](../../../../translated_images/pcm/energy-plot.5fdac3f397a910bc.webp)
 
 4. Now, plot di first week of July 2014, by providing am as input to di `energy` in `[from date]: [to date]` pattern:
 
@@ -171,7 +171,7 @@ Make we start to create time series model to predict future power usage based on
     plt.show()
     ```
 
-    ![july](../../../../translated_images/july-2014.9e1f7c318ec6d5b3.pcm.png)
+    ![july](../../../../translated_images/pcm/july-2014.9e1f7c318ec6d5b3.webp)
 
     Di plot fine well! Check di plots and see if you fit identify any of di characteristics wey we list before. Wetin you fit learn by visualizing di data?
 
diff --git a/translations/pcm/7-TimeSeries/2-ARIMA/README.md b/translations/pcm/7-TimeSeries/2-ARIMA/README.md
index fd2d5b943..0a627b708 100644
--- a/translations/pcm/7-TimeSeries/2-ARIMA/README.md
+++ b/translations/pcm/7-TimeSeries/2-ARIMA/README.md
@@ -114,7 +114,7 @@ Now we don load di data, so we fit separate am into train and test sets. You go
     plt.show()
     ```
 
-    ![training and testing data](../../../../translated_images/train-test.8928d14e5b91fc94.pcm.png)
+    ![training and testing data](../../../../translated_images/pcm/train-test.8928d14e5b91fc94.webp)
 
     So, using small time window for training di data go dey okay.
 
@@ -157,11 +157,11 @@ Now, you need prepare di data for training by filtering and scaling di data. Fil
     plt.show()
     ```
 
-    ![original](../../../../translated_images/original.b2b15efe0ce92b87.pcm.png)
+    ![original](../../../../translated_images/pcm/original.b2b15efe0ce92b87.webp)
 
     > Di original data
 
-    ![scaled](../../../../translated_images/scaled.e35258ca5cd3d43f.pcm.png)
+    ![scaled](../../../../translated_images/pcm/scaled.e35258ca5cd3d43f.webp)
 
     > Di scaled data
 
@@ -321,7 +321,7 @@ Check di accuracy of di model by testing di mean absolute percentage error (MAPE
 
 > **🧮 Show me di math**
 >
-> ![MAPE](../../../../translated_images/mape.fd87bbaf4d346846.pcm.png)
+> ![MAPE](../../../../translated_images/pcm/mape.fd87bbaf4d346846.webp)
 >
 >  [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) na wetin dem dey use to show how prediction accurate dey as ratio wey dem define by di formula wey dey up. Di difference wey dey between actual<sub>t</sub> and predicted<sub>t</sub> dem dey divide am by di actual<sub>t</sub>. "Di absolute value for dis calculation na di sum for every forecasted point for time, and dem go divide am by di number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
 
@@ -381,7 +381,7 @@ Check di accuracy of di model by testing di mean absolute percentage error (MAPE
     plt.show()
     ```
 
-    ![a time series model](../../../../translated_images/accuracy.2c47fe1bf15f44b3.pcm.png)
+    ![a time series model](../../../../translated_images/pcm/accuracy.2c47fe1bf15f44b3.webp)
 
 🏆 Dis graph fine well well, e show say di model get good accuracy. Good job!
 
diff --git a/translations/pcm/7-TimeSeries/3-SVR/README.md b/translations/pcm/7-TimeSeries/3-SVR/README.md
index e06f72a32..354eb2e27 100644
--- a/translations/pcm/7-TimeSeries/3-SVR/README.md
+++ b/translations/pcm/7-TimeSeries/3-SVR/README.md
@@ -71,7 +71,7 @@ Open di [_/working_](https://github.com/microsoft/ML-For-Beginners/tree/main/7-T
    plt.show()
    ```
 
-   ![full data](../../../../translated_images/full-data.a82ec9957e580e97.pcm.png)
+   ![full data](../../../../translated_images/pcm/full-data.a82ec9957e580e97.webp)
 
    Now, make we build our SVR model.
 
@@ -97,7 +97,7 @@ Now wey your data don load, you fit separate am into train and test sets. Then y
    plt.show()
    ```
 
-   ![training and testing data](../../../../translated_images/train-test.ead0cecbfc341921.pcm.png)
+   ![training and testing data](../../../../translated_images/pcm/train-test.ead0cecbfc341921.webp)
 
 
 
@@ -275,7 +275,7 @@ plt.title("Training data prediction")
 plt.show()
 ```
 
-![training data prediction](../../../../translated_images/train-data-predict.3c4ef4e78553104f.pcm.png)
+![training data prediction](../../../../translated_images/pcm/train-data-predict.3c4ef4e78553104f.webp)
 
 Print MAPE for training data
 
@@ -298,7 +298,7 @@ plt.xlabel('Timestamp')
 plt.show()
 ```
 
-![testing data prediction](../../../../translated_images/test-data-predict.8afc47ee7e52874f.pcm.png)
+![testing data prediction](../../../../translated_images/pcm/test-data-predict.8afc47ee7e52874f.webp)
 
 Print MAPE for testing data
 
@@ -354,7 +354,7 @@ plt.xlabel('Timestamp')
 plt.show()
 ```
 
-![full data prediction](../../../../translated_images/full-data-predict.4f0fed16a131c8f3.pcm.png)
+![full data prediction](../../../../translated_images/pcm/full-data-predict.4f0fed16a131c8f3.webp)
 
 ```python
 print('MAPE: ', mape(Y_pred, Y)*100, '%')
diff --git a/translations/pcm/7-TimeSeries/README.md b/translations/pcm/7-TimeSeries/README.md
index 15bcfbd69..952e45dd6 100644
--- a/translations/pcm/7-TimeSeries/README.md
+++ b/translations/pcm/7-TimeSeries/README.md
@@ -17,7 +17,7 @@ For dis two lessons, you go sabi wetin time series forecasting be, one area for
 
 Di regional focus na di electricity usage for di world, one kind dataset wey dey interesting to take learn how to forecast di power wey people go use for future based on di pattern of di load wey dem don use before. You go see as dis kind forecasting fit dey very useful for business work.
 
-![electric grid](../../../translated_images/electric-grid.0c21d5214db09ffa.pcm.jpg)
+![electric grid](../../../translated_images/pcm/electric-grid.0c21d5214db09ffa.webp)
 
 Photo by [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) of electrical towers on a road in Rajasthan on [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
 
diff --git a/translations/pcm/8-Reinforcement/1-QLearning/README.md b/translations/pcm/8-Reinforcement/1-QLearning/README.md
index 7c2560325..61c410f57 100644
--- a/translations/pcm/8-Reinforcement/1-QLearning/README.md
+++ b/translations/pcm/8-Reinforcement/1-QLearning/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Introduction to Reinforcement Learning and Q-Learning
 
-![Summary of reinforcement in machine learning in a sketchnote](../../../../translated_images/ml-reinforcement.94024374d63348db.pcm.png)
+![Summary of reinforcement in machine learning in a sketchnote](../../../../translated_images/pcm/ml-reinforcement.94024374d63348db.webp)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 Reinforcement learning get three main things: agent, states, and actions wey dey for each state. If agent do action for one state, e go get reward. Imagine say you dey play Super Mario game. You be Mario, you dey one level for the game, you stand near cliff edge. Coin dey above you. You be Mario, for one level, for one position... na your state be dat. If you waka go right (action), you go fall for cliff and your score go low. But if you press jump button, you go collect coin and you go still dey alive. Dat one na better result and e go give you better score.
@@ -40,7 +40,7 @@ For this lesson, we go look the world of **[Peter and the Wolf](https://en.wikip
 
 Make we make Peter world simple, e go be one square board wey get size `width` x `height`, like this:
 
-![Peter's Environment](../../../../translated_images/environment.40ba3cb66256c93f.pcm.png)
+![Peter's Environment](../../../../translated_images/pcm/environment.40ba3cb66256c93f.webp)
 
 Each cell for this board fit be:
 
@@ -177,7 +177,7 @@ Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)
 
 Notice say we dey initialize all the values for the Q-Table with equal value, for our case - 0.25. This one mean "random walk" policy, because all moves for each state dey equally good. We fit pass the Q-Table to the `plot` function to visualize the table on the board: `m.plot(Q)`.
 
-![Peter's Environment](../../../../translated_images/env_init.04e8f26d2d60089e.pcm.png)
+![Peter's Environment](../../../../translated_images/pcm/env_init.04e8f26d2d60089e.webp)
 
 For the center of each cell, arrow dey show the preferred direction of movement. Since all directions dey equal, dot go show.
 
@@ -195,7 +195,7 @@ Suppose we dey state *s*, and we wan move to next state *s'*. If we do am, we go
 
 This give us **Bellman formula** to calculate Q-Table value for state *s*, given action *a*:
 
-<img src="../../../../translated_images/bellman-equation.7c0c4c722e5a6b7c.pcm.png"/>
+<img src="../../../../translated_images/pcm/bellman-equation.7c0c4c722e5a6b7c.webp"/>
 
 Here γ na **discount factor** wey dey decide how you go prefer current reward over future reward.
 
@@ -267,7 +267,7 @@ Run the learning algorithm for 5000 experiments, wey we dey call **epochs**: (co
 
 After we run this algorithm, the Q-Table go update with values wey show how good different actions dey for each step. We fit try visualize the Q-Table by drawing vector for each cell wey go point the direction of movement. To make am simple, we go draw small circle instead of arrow head.
 
-<img src="../../../../translated_images/learned.ed28bcd8484b5287.pcm.png"/>
+<img src="../../../../translated_images/pcm/learned.ed28bcd8484b5287.webp"/>
 
 ## Checking the policy
 
@@ -311,7 +311,7 @@ After you run dis code, you suppose see say di average path length go reduce wel
 
 As we don talk before, di learning process na balance between exploration and di knowledge wey we don gather about di problem space. We don see say di result of di learning (di ability to help agent find short path go di goal) don improve, but e go still make sense to check how di average path length dey behave during di learning process:
 
-<img src="../../../../translated_images/lpathlen1.0534784add58d4eb.pcm.png"/>
+<img src="../../../../translated_images/pcm/lpathlen1.0534784add58d4eb.webp"/>
 
 Di learnings fit summarize like dis:
 
diff --git a/translations/pcm/8-Reinforcement/2-Gym/README.md b/translations/pcm/8-Reinforcement/2-Gym/README.md
index 2b70f7cc5..dac549c9c 100644
--- a/translations/pcm/8-Reinforcement/2-Gym/README.md
+++ b/translations/pcm/8-Reinforcement/2-Gym/README.md
@@ -19,13 +19,13 @@ For dis lesson, we go use di same Q-Learning principles for problem wey get **co
 
 > **Problem**: If Peter wan run comot from di wolf, e need sabi move fast. We go see how Peter go fit learn how to skate, especially to balance, using Q-Learning.
 
-![Di great escape!](../../../../translated_images/escape.18862db9930337e3.pcm.png)
+![Di great escape!](../../../../translated_images/pcm/escape.18862db9930337e3.webp)
 
 > Peter and im padi dem dey creative to run comot from di wolf! Image by [Jen Looper](https://twitter.com/jenlooper)
 
 We go use one simple version of balancing wey dem dey call **CartPole** problem. For di cartpole world, we get one horizontal slider wey fit move left or right, and di goal na to balance one vertical pole for di top of di slider.
 
-<img alt="a cartpole" src="../../../../translated_images/cartpole.b5609cc0494a14f7.pcm.png" width="200"/>
+<img alt="a cartpole" src="../../../../translated_images/pcm/cartpole.b5609cc0494a14f7.webp" width="200"/>
 
 ## Prerequisites
 
@@ -285,7 +285,7 @@ During training, we don collect di cumulative reward value for each of di iterat
 plt.plot(rewards)
 ```
 
-![raw progress](../../../../translated_images/train_progress_raw.2adfdf2daea09c59.pcm.png)
+![raw progress](../../../../translated_images/pcm/train_progress_raw.2adfdf2daea09c59.webp)
 
 From dis graph, e no dey possible to talk anything, because di stochastic training process dey make di length of training sessions vary well. To make di graph make sense, we fit calculate di **running average** over series of experiments, like 100. We fit do dis well using `np.convolve`: (code block 12)
 
@@ -296,7 +296,7 @@ def running_average(x,window):
 plt.plot(running_average(rewards,100))
 ```
 
-![training progress](../../../../translated_images/train_progress_runav.c71694a8fa9ab359.pcm.png)
+![training progress](../../../../translated_images/pcm/train_progress_runav.c71694a8fa9ab359.webp)
 
 ## Varying hyperparameters
 
diff --git a/translations/pcm/8-Reinforcement/2-Gym/assignment.md b/translations/pcm/8-Reinforcement/2-Gym/assignment.md
index 2c7a75e64..0df79a536 100644
--- a/translations/pcm/8-Reinforcement/2-Gym/assignment.md
+++ b/translations/pcm/8-Reinforcement/2-Gym/assignment.md
@@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 [Mountain Car environment](https://gym.openai.com/envs/MountainCar-v0/) get one car wey dey stuck for valley:
 
-<img src="../../../../translated_images/mountaincar.43d56e588ce581c2.pcm.png" width="300"/>
+<img src="../../../../translated_images/pcm/mountaincar.43d56e588ce581c2.webp" width="300"/>
 
 Di goal na to commot for di valley and capture di flag, by doing one of di actions wey dey below for each step:
 
diff --git a/translations/pcm/8-Reinforcement/README.md b/translations/pcm/8-Reinforcement/README.md
index 10e2eb0c8..b8e7ef17e 100644
--- a/translations/pcm/8-Reinforcement/README.md
+++ b/translations/pcm/8-Reinforcement/README.md
@@ -13,7 +13,7 @@ Reinforcement learning, RL, na one of di main machine learning style, e dey foll
 
 Imagine say you get one simulated environment like stock market. Wetin go happen if you put one regulation? E go get positive or negative effect? If e get negative effect, you go need take di _negative reinforcement_, learn from am, and change wetin you dey do. If e get positive result, you go need build on top di _positive reinforcement_.
 
-![peter and the wolf](../../../translated_images/peter.779730f9ba3a8a8d.pcm.png)
+![peter and the wolf](../../../translated_images/pcm/peter.779730f9ba3a8a8d.webp)
 
 > Peter and im friends wan run comot from di hungry wolf! Image by [Jen Looper](https://twitter.com/jenlooper)
 
diff --git a/translations/pcm/9-Real-World/1-Applications/README.md b/translations/pcm/9-Real-World/1-Applications/README.md
index 4fedbbb23..30b03301d 100644
--- a/translations/pcm/9-Real-World/1-Applications/README.md
+++ b/translations/pcm/9-Real-World/1-Applications/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Postscript: Machine learning for real world
 
-![Summary of machine learning in the real world in a sketchnote](../../../../translated_images/ml-realworld.26ee274671615577.pcm.png)
+![Summary of machine learning in the real world in a sketchnote](../../../../translated_images/pcm/ml-realworld.26ee274671615577.webp)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 For dis curriculum, you don learn plenty ways to prepare data for training and create machine learning models. You don build different types of models like regression, clustering, classification, natural language processing, and time series models. Congrats! Now, you fit dey wonder wetin all dis work mean... wetin be the real world use for dis models?
diff --git a/translations/pcm/9-Real-World/2-Debugging-ML-Models/README.md b/translations/pcm/9-Real-World/2-Debugging-ML-Models/README.md
index 806b10b64..031c4f065 100644
--- a/translations/pcm/9-Real-World/2-Debugging-ML-Models/README.md
+++ b/translations/pcm/9-Real-World/2-Debugging-ML-Models/README.md
@@ -34,17 +34,17 @@ Before you start, abeg review [Responsible AI tools for developers](https://www.
 
 Di normal way to measure model performance na to check di correct vs wrong predictions. For example, if model dey accurate 89% of di time and error loss na 0.001, e fit look like say di model dey perform well. But di errors no dey spread equally for di data. You fit get 89% accuracy but find out say di model dey fail 42% of di time for some parts of di data. Dis kind failure fit cause fairness or reliability wahala. E dey important to sabi where di model dey perform well or dey fail. Di parts of di data wey get plenty errors fit be important data group.
 
-![Analyze and debug model errors](../../../../translated_images/ea-error-distribution.117452e1177c1dd8.pcm.png)
+![Analyze and debug model errors](../../../../translated_images/pcm/ea-error-distribution.117452e1177c1dd8.webp)
 
 Di Error Analysis component for RAI dashboard dey show how di model dey fail for different groups with tree visualization. E dey help to find di features or areas wey get high error rate for di data. Once you sabi where di errors dey come from, you fit start to check di root cause. You fit also create data groups to analyze. Dis data groups dey help to debug why di model dey perform well for one group but dey fail for another.
 
-![Error Analysis](../../../../translated_images/ea-error-cohort.6886209ea5d438c4.pcm.png)
+![Error Analysis](../../../../translated_images/pcm/ea-error-cohort.6886209ea5d438c4.webp)
 
 Di tree map dey show di problem areas fast. For example, di darker di red color for di tree node, di higher di error rate.
 
 Heat map na another visualization wey users fit use to check di error rate with one or two features to find wetin dey cause di model errors for di whole dataset or groups.
 
-![Error Analysis Heatmap](../../../../translated_images/ea-heatmap.8d27185e28cee383.pcm.png)
+![Error Analysis Heatmap](../../../../translated_images/pcm/ea-heatmap.8d27185e28cee383.webp)
 
 Use error analysis when you need to:
 
@@ -57,11 +57,11 @@ To check how machine learning model dey perform, you need to understand di behav
 
 Di Model Overview component for RAI dashboard dey help to analyze di performance metrics for di data representation for one group, and e dey allow users compare di model behavior for different groups.
 
-![Dataset cohorts - model overview in RAI dashboard](../../../../translated_images/model-overview-dataset-cohorts.dfa463fb527a35a0.pcm.png)
+![Dataset cohorts - model overview in RAI dashboard](../../../../translated_images/pcm/model-overview-dataset-cohorts.dfa463fb527a35a0.webp)
 
 Di feature-based analysis functionality dey allow users narrow down data subgroups for one feature to find wahala for detailed level. For example, di dashboard fit automatically create groups for one feature wey user select (like *"time_in_hospital < 3"* or *"time_in_hospital >= 7"*). Dis dey allow user isolate one feature from di bigger data group to see if e dey cause di model errors.
 
-![Feature cohorts - model overview in RAI dashboard](../../../../translated_images/model-overview-feature-cohorts.c5104d575ffd0c80.pcm.png)
+![Feature cohorts - model overview in RAI dashboard](../../../../translated_images/pcm/model-overview-feature-cohorts.c5104d575ffd0c80.webp)
 
 Di Model Overview component dey support two types of disparity metrics:
 
@@ -85,7 +85,7 @@ Data na big blind spot for di normal model performance metrics. You fit get high
 
 Di Data Analysis component for RAI dashboard dey help to find areas wey di data dey represent one group too much or too small. E dey help users check di root cause of errors and fairness wahala wey di data imbalance or lack of representation dey cause. E dey allow users visualize datasets based on predicted and actual outcomes, error groups, and specific features. Sometimes, if you find one group wey di data no represent well, e fit show say di model no dey learn well, and na why di errors dey plenty. If model get data bias, e no just be fairness wahala but e show say di model no dey inclusive or reliable.
 
-![Data Analysis component on RAI Dashboard](../../../../translated_images/dataanalysis-cover.8d6d0683a70a5c1e.pcm.png)
+![Data Analysis component on RAI Dashboard](../../../../translated_images/pcm/dataanalysis-cover.8d6d0683a70a5c1e.webp)
 
 Use data analysis when you need to:
 
@@ -104,14 +104,14 @@ Machine learning models dey like black box. To sabi which key data features dey
 
 Di Feature Importance component for RAI dashboard dey help you debug and understand how di model dey make predictions. E dey useful for machine learning professionals and decision-makers to explain and show evidence of features wey dey influence di model behavior for regulatory compliance. Users fit check both global and local explanations to confirm which features dey drive di model predictions. Global explanations dey show di top features wey dey affect di model overall prediction. Local explanations dey show di features wey make di model predict something for one case. To check local explanations dey help debug or audit one case to understand why di model make correct or wrong prediction.
 
-![Feature Importance component of the RAI dashboard](../../../../translated_images/9-feature-importance.cd3193b4bba3fd4b.pcm.png)
+![Feature Importance component of the RAI dashboard](../../../../translated_images/pcm/9-feature-importance.cd3193b4bba3fd4b.webp)
 
 * Global explanations: For example, which features dey affect di overall behavior of diabetes hospital readmission model?
 * Local explanations: For example, why di model predict say diabetic patient wey dey over 60 years old with prior hospitalizations go return or no go return to hospital within 30 days?
 
 For di debugging process to check di model performance for different groups, Feature Importance dey show di level of impact wey feature get for di groups. E dey help find wahala when you compare di level of influence wey di feature get for di model errors. Di Feature Importance component fit show which values for feature dey positively or negatively affect di model outcome. For example, if di model make wrong prediction, di component go allow you check wetin for di features or feature values cause di prediction. Dis level of detail dey help not just for debugging but e dey bring transparency and accountability for auditing situations. Finally, di component fit help you find fairness wahala. For example, if sensitive feature like ethnicity or gender dey highly influence di model prediction, e fit show race or gender bias for di model.
 
-![Feature importance](../../../../translated_images/9-features-influence.3ead3d3f68a84029.pcm.png)
+![Feature importance](../../../../translated_images/pcm/9-features-influence.3ead3d3f68a84029.webp)
 
 Use interpretability when you need to:
 
diff --git a/translations/pcm/9-Real-World/README.md b/translations/pcm/9-Real-World/README.md
index 36f3da451..d7853aacb 100644
--- a/translations/pcm/9-Real-World/README.md
+++ b/translations/pcm/9-Real-World/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 For dis part of di curriculum, you go sabi some real-world ways wey dem dey use classical ML. We don waka internet well well find whitepapers and articles wey talk about how dem don use dis strategies, we dey try avoid neural networks, deep learning and AI as much as we fit. You go learn how ML dey work for business systems, ecological applications, finance, arts and culture, and plenty other areas.
 
-![chess](../../../translated_images/chess.e704a268781bdad8.pcm.jpg)
+![chess](../../../translated_images/pcm/chess.e704a268781bdad8.webp)
 
 > Foto by <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> for <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/pcm/README.md b/translations/pcm/README.md
index 5f9123060..5de3b4a1c 100644
--- a/translations/pcm/README.md
+++ b/translations/pcm/README.md
@@ -1,86 +1,83 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T14:26:32+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T17:25:01+00:00",
   "source_file": "README.md",
   "language_code": "pcm"
 }
 -->
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-
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-
 ### 🌐 Multi-Language Support
 
 #### Supported via GitHub Action (Automated & Always Up-to-Date)
 
-<!-- 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](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](./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)
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### Make una join our community
+> **Prefer to Clone Locally?**
+
+> Dis repository get more pass 50 language translations wey go big pass di download size. To clone without di translations, use 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'
+> ```
+> Dis go gi you everytin wey you need to finish di course with beta fast download.
+  
+#### Join Our Community
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-We get one Discord "Learn with AI" series wey dey go on — learn more and join us for [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You go get beta tips and tricks on how to use GitHub Copilot for Data Science.
+We get Discord learn with AI series wey dey go on, learn more and join us for [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You go get tips and tricks for how to use GitHub Copilot for Data Science.
 
-![Learn wit AI series](../../translated_images/3.9b58fd8d6c373c20.pcm.png)
+![Learn with AI series](../../../../translated_images/pcm/3.9b58fd8d6c373c20.webp)
 
 # Machine Learning for Beginners - A Curriculum
 
-> 🌍 Make we waka round di world as we dey explore Machine Learning through different cultures 🌍
+> 🌍 Travel around di world as we explore Machine Learning using world cultures 🌍
 
-Cloud Advocates for Microsoft dey happy to give una one 12-week, 26-lesson curriculum wey dey all about **Machine Learning**. For dis curriculum, you go learn wetin people dey call **classic machine learning**, we go mainly use Scikit-learn as library and sidon comot deep learning, wey dey inside our [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) too!
+Cloud Advocates for Microsoft dey happy to offer 12-week, 26-lesson curriculum all about **Machine Learning**. For dis curriculum, you go learn wetin dem dey call **classic machine learning**, normally using Scikit-learn as library and we no dey do deep learning wey dey for our [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pair dis lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners), too!
 
-Waka with us round di world as we apply these classic techniques to data wey come from plenty different places. Every lesson get pre- and post-lesson quizzes, written instructions to finish di lesson, one solution, one assignment, and more. Our project-based way of teaching make you dey learn as you dey build — na better way make new skills hold.
+Travel with us around di world as we apply dis classic techniques to data from different parts of di world. Every lesson get pre- and post-lesson quizzes, written instructions to complete di lesson, solution, assignment, and more. Our project-based way of teaching make you dey learn while you dey build, na beta way for new skills to "stick".
 
-**✍️ Big tnx to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ Big thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
 
-**🎨 Tnx to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
+**🎨 Thanks to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
 
-**🙏 Special tnx 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors**, especially Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+**🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors**, especially Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 
-**🤩 Extra tnx to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**
+**🤩 Extra thanks to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**
 
-# How to start
+# Getting Started
 
 Follow these steps:
-1. **Fork di Repository**: Click on the "Fork" button for di top-right corner of dis page.
-2. **Clone di Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **Fork the Repository**: Click di "Fork" button for top-right corner of dis page.
+2. **Clone the Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-> 🔧 **Need help?** Check our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common issues with installation, setup, and running lessons.
+> [find all additional resources for dis course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **Need help?** Check our [Troubleshooting Guide](TROUBLESHOOTING.md) for solutions to common wahalas with installation, setup, and running lessons.
 
-**[Students](https://aka.ms/student-page)**, to use this curriculum, fork the whole repo to your own GitHub account and do the exercises on your own or with group:
+**[Students](https://aka.ms/student-page)**, to use dis curriculum, fork di whole repo to your own GitHub account and complete di exercises for your own or with group:
 
-- Start wit pre-lecture quiz.
-- Read di lecture and do di activities, stop small and reason for each knowledge check.
-- Try build di projects by understanding di lessons instead of just running di solution code; still di code dey for `/solution` folders for each project-oriented lesson.
-- Do di post-lecture quiz.
-- Finish di challenge.
-- Finish di assignment.
-- After you don finish one lesson group, visit di [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling di correct PAT rubric. PAT na Progress Assessment Tool — na one rubric wey you go fill to help your learning. You fit also react to other PATs so we all fit learn together.
+- Start with pre-lecture quiz.
+- Read di lecture and complete di activities, stop and think for every knowledge check.
+- Try to build di projects by understanding di lessons instead of just running di solution code; but dat code dey for `/solution` folders for each project lesson.
+- Take di post-lecture quiz.
+- Complete di challenge.
+- Complete di assignment.
+- After you finish lesson group, visit di [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling di correct PAT rubric. A 'PAT' na Progress Assessment Tool wey be rubric wey you go fill to help your learning. You fit also react to other PATs so we fit learn together.
 
-> If you wan study more, we recommend make you follow these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules and learnin paths.
+> For more study, we recommend to follow dis [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules and learning paths.
 
-**Teachers**, we don [include some suggestions](for-teachers.md) on how make you use this curriculum.
+**Teachers**, we don [put some suggestions](for-teachers.md) on how to use dis curriculum.
 
 ---
 
 ## Video walkthroughs
 
-Some lessons get short video form. You fit find dem inside di lessons, or for di [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) by clicking di image below.
+Some lessons get short video form. You fit find all these inside di lessons, or for di [ML for Beginners playlist for Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) by clicking di image below.
 
-[![ML for beginners banner](../../translated_images/ml-for-beginners-video-banner.63f694a100034bc6.pcm.png)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/pcm/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
@@ -90,17 +87,17 @@ Some lessons get short video form. You fit find dem inside di lessons, or for di
 
 **Gif by** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Click di image wey dey above for video about di project and di people wey build am!
+> 🎥 Click di image up top for video about di project and di pipo wey create am!
 
 ---
 
 ## Pedagogy
 
-We pick two teaching principles when we dey build this curriculum: make am hands-on and **project-based**, and include **frequent quizzes**. Also, dis curriculum get one common **theme** to hold everything together.
+We choose two teaching principles while we dey build dis curriculum: to make am hands-on **project-based** and to put **frequent quizzes**. Also, dis curriculum get one **theme** to make everything gel well.
 
-By make content follow projects, students go dey more engaged and dem go remember di concepts well. Plus, one low-stakes quiz before class dey set student intention, and another quiz after class go help memory stay. This curriculum make am flexible and fun — you fit take am full or small part. Projects start small and dem go dey more complex as di 12-week cycle dey finish. E still get one postscript about how ML dey work for real world, wey teachers fit use as extra credit or base for discussion.
+By making sure say di content match projects, e make di learning dey more interesting for students and e go help dem remember di concepts better. Plus, low-stakes quiz before class dey set di student mind to learn di topic, while di second quiz after class dey help remember am more. Dis curriculum na to be easy and fun, and you fit do am whole or part. Di projects start small and dem go dey more complex by di end of di 12-week cycle. Dis curriculum get postscript on how ML dey work for real world, wey fit be extra credit or basis for talk.
 
-> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), and [Troubleshooting](TROUBLESHOOTING.md) guidelines. We dey welcome your constructive feedback!
+> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), and [Troubleshooting](TROUBLESHOOTING.md) guidelines. We welcome your constructive feedback!
 
 ## Each lesson includes
 
@@ -109,36 +106,35 @@ By make content follow projects, students go dey more engaged and dem go remembe
 - video walkthrough (some lessons only)
 - [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
 - written lesson
-- for project-based lessons, step-by-step guides on how to build the project
+- for project-based lessons, step-by-step guides on how to build di project
 - knowledge checks
-- a challenge
+- challenge
 - supplemental reading
 - assignment
 - [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
 
-> **A note about languages**: These lessons mainly dem write for Python, but many still dey available for R. To finish an R lesson, go to di `/solution` folder and find di R lessons. Dem get an .rmd extension wey mean R Markdown file — na basically Markdown wey fit hold `code chunks` (R or other languages) and one `YAML header` (wey dey guide how outputs go format like PDF). As e be, e good for data science authoring cos e allow you join your code, di output, and your notes inside Markdown. R Markdown documents fit render go output formats like PDF, HTML, or Word.
-
-> **A note about quizzes**: All quizzes dey inside di [Quiz App folder](../../quiz-app), total 52 quizzes, each get three questions. Dem link the quizzes from di lessons but you fit run di quiz app locally; follow di instruction inside the `quiz-app` folder to host am locally or deploy to Azure.
+> **Talk about languages**: These lessons mainly dey written for Python, but many dey also available for R. To finish R lesson, go di `/solution` folder and look for R lessons. Dem get .rmd extension wey mean **R Markdown** file wey be embedding of `code chunks` (R or other languages) and `YAML header` (which shows how to format output like PDF) inside one `Markdown document`. So, e act as good writing framework for data science because e allow you put your code, output, and your thoughts down for Markdown. More so, R Markdown documents fit render output formats like PDF, HTML, or Word.
+> **One note about quizzes**: All quizzes dey for [Quiz App folder](../../quiz-app), get total 52 quizzes with three questions each. Dem link dem from inside the lessons but you fit run the quiz app for your local machine; follow the instruction inside the `quiz-app` folder make you fit run am local or deploy am to Azure.
 
 | Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
 | :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Intro wey dey explain machine learning                |      [Introduction](1-Introduction/README.md)       | Learn di basic concepts wey dey behind machine learning                                                                                |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Di history of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn di history wey dey under dis field                                                                                         |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | Wetin be di important philosophical mata about fairness wey students suppose consider when dem dey build an apply ML models? |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Techniques wey dem dey use for machine learning                 |      [Introduction](1-Introduction/README.md)       | Which techniques ML researchers dey use to build ML models?                                                                       |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Start to use Python and Scikit-learn for regression models                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data to prepare for ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      01       |                Introduction to machine learning                |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | Wetin important philosophical issues around fairness dat students suppose consider when dem dey build and apply ML models?      |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | Wetin techniques ML researchers dey use to build ML models?                                                                     |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Start to use Python and Scikit-learn for regression models                                                                      |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Make data visual and clean am so e go ready for ML                                                                               |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
 |      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build logistic regression model                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build web app wey go use your trained model                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prepare, and visualize your data; introduction to classification                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build one logistic regression model                                                                                             |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build web app to take use your trained model                                                                                    |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prepare, and visualise your data; introduction to classification                                                         | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
 |      11       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [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       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [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       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Build recommender web app wey dey use your model                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prepare, and visualize your data; Introduction to clustering                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore di K-Means clustering method                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Introduction to natural language processing ☕️         |   [Natural language processing](6-NLP/README.md)    | Learn di basics about NLP by building a simple bot                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Common NLP Tasks ☕️                      |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks wey dey necessary when you dey deal with language structures                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      13       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Build recommender web app with your model                                                                                       |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prepare, and visualise your data; Introduction to clustering                                                             |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore K-Means clustering method                                                                                              |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|      16       |        Introduction to natural language processing ☕️         |   [Natural language processing](6-NLP/README.md)    | Learn basics about NLP by building one simple bot                                                                               |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17       |                      Common NLP Tasks ☕️                      |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks wey you suppose do when you dey work with language structures           |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
 |      18       |             Translation and sentiment analysis ♥️              |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
 |      19       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
 |      20       |                  Romantic hotels of Europe ♥️                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
@@ -148,22 +144,22 @@ By make content follow projects, students go dey more engaged and dem go remembe
 |      24       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
 |      25       |                 Help Peter avoid the wolf! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
 |  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            Model Debugging in ML using RAI dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+|  Postscript   |            Model Debugging in ML using RAI dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                    |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                   |
 
 > [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-## Access wey no need internet
+## Offline access
 
-You fit run dis documentation offline by using [Docsify](https://docsify.js.org/#/). Fork dis repo, [install Docsify](https://docsify.js.org/#/quickstart) for your local machine, and then for the root folder of dis repo, type `docsify serve`. Di website go dey served for port 3000 on your localhost: `localhost:3000`.
+You fit run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) for your local machine, then for the root folder of this repo, type `docsify serve`. The website go run for port 3000 for your localhost: `localhost:3000`.
 
 ## PDFs
 
-Find pdf of di curriculum wit links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+Find one pdf of the curriculum with links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
-## 🎒 Oda Courses 
+## 🎒 Other Courses 
 
-Our team dey produce oda courses! Check dem out:
+Our team dey produce other courses! Check am out:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -188,36 +184,36 @@ Our team dey produce oda courses! Check dem out:
 
 ---
  
-### Main Tin Wey You Go Learn
-[![ML for people wey dey start](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 people wey dey start](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 people wey dey start](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 people wey dey start](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 people wey dey start](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 people wey dey start](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 people wey dey start](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)
+### Core Learning
+[![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 Series
-[![Copilot for AI wey dey Pair-Program](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 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 -->
 
-## How to Get Help
+## Getting Help
 
-If you jam stuck or get any question about how to build AI apps, join other learners and experienced developers for discussion about MCP. Na supportive community wey dey welcome questions and where people dey share knowledge freely.
+If you get stuck or get any questions about how to build AI apps. Join other people wey dey learn and experienced developers for talk about MCP. E be supportive community wey questions dey welcome and knowledge dey share freely.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-If you get product feedback or you see errors while you dey build, visit:
+If you get product feedback or you see mistake wen you dey build, come visit:
 
 [![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 -->
-Abeg note:
-We use AI translation service (Co-op Translator: https://github.com/Azure/co-op-translator) take translate dis document. Even though we dey try make am correct, make you sabi say automatic translations fit get mistakes or wrong parts. The original document for im own language na the main source wey get authority. If na important information, make you use professional human translator. We no be liable for any misunderstanding or misinterpretation wey fit happen because of this translation.
+**Disclaimer**:  
+Dis document na so AI translation service wey dem call [Co-op Translator](https://github.com/Azure/co-op-translator) translate am. Even though we dey try make everything correct, abeg sabi say automatic translation fit get some mistakes or no too correct. If you get original document for the original language, na im be correct source. For important information, e better make person wey sabi do translation do am. We no go take responsibility if person no understand well or wrong meaning comot from dis translation.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/ta/README.md b/translations/ta/README.md
index 136fcd5e7..6cb484618 100644
--- a/translations/ta/README.md
+++ b/translations/ta/README.md
@@ -1,223 +1,231 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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-  "translation_date": "2026-01-01T14:14:51+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T17:21:23+00:00",
   "source_file": "README.md",
   "language_code": "ta"
 }
 -->
-[![GitHub உரிமம்](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub பங்களிப்பாளர்கள்](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub பிரச்சனைகள்](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![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)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub கண்காணிப்பவர்கள்](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub Forkகள்](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub நட்சத்திரங்கள்](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/)
 
-### 🌐 பல மொழி ஆதரவு
+### 🌐 பன்மொழி ஆதரவிடம்
 
-#### GitHub Action மூலம் ஆதரிக்கப்படுகிறது (தானியங்கி & எப்போதும் புதுப்பிக்கப்பட்டுள்ளது)
+#### GitHub Action மூலம் ஆதரவு (தானியங்கி மற்றும் எப்போதும் புதுப்பித்தல்)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[அரபு](../ar/README.md) | [பெங்காலி](../bn/README.md) | [பல்கேரியன்](../bg/README.md) | [பர்மீஸ் (மியான்மார்)](../my/README.md) | [சீனம் (எளிமைப்படுத்தப்பட்டது)](../zh/README.md) | [சீனம் (பாரம்பரிய, ஹொங்க் காங்)](../hk/README.md) | [சீனம் (பாரம்பரிய, மாகாவ்)](../mo/README.md) | [சீனம் (பாரம்பரிய, தைவான்)](../tw/README.md) | [குரோயேஷியன்](../hr/README.md) | [செக்](../cs/README.md) | [டேனிஷ்](../da/README.md) | [டச்சு](../nl/README.md) | [எஸ்டோனியன்](../et/README.md) | [ஃபின்னிஷ்](../fi/README.md) | [பிரெஞ்சு](../fr/README.md) | [ஜெர்மன்](../de/README.md) | [கிரேக்க](../el/README.md) | [ஹீப்ரூ](../he/README.md) | [இந்தி](../hi/README.md) | [ஹங்கேரியன்](../hu/README.md) | [இந்தோனேஷியன்](../id/README.md) | [இத்தாலியன்](../it/README.md) | [ஜப்பானீஸ்](../ja/README.md) | [கன்னடம்](../kn/README.md) | [கொரியன்](../ko/README.md) | [லிதுவேனியன்](../lt/README.md) | [மலாய்](../ms/README.md) | [மலையாளம்](../ml/README.md) | [மராத்தி](../mr/README.md) | [நேபாளம்](../ne/README.md) | [நைஜீரியன் பிகின்](../pcm/README.md) | [நோர்வேஜியன்](../no/README.md) | [பேர்ஷியன் (ஃபார்ஸி)](../fa/README.md) | [பொலிஷ்](../pl/README.md) | [போர்ச்சுகீஸ் (பிரேசில்)](../br/README.md) | [போர்ச்சுகீஸ் (போர்ச்சுகல்)](../pt/README.md) | [பஞ்சாப்பி (குர்முகி)](../pa/README.md) | [ரோமானியன்](../ro/README.md) | [ரஷ்யன்](../ru/README.md) | [சர்பியன் (சைரிலிக்)](../sr/README.md) | [ஸ்லோவாக்](../sk/README.md) | [ஸ்லோவேனியன்](../sl/README.md) | [ஸ்பானிஷ்](../es/README.md) | [ஸ்வாஹிலி](../sw/README.md) | [ஸ்வீடிஷ்](../sv/README.md) | [டாகாலோக (பிலிப்பைன்ஸ்)](../tl/README.md) | [தமிழ்](./README.md) | [తెలుగు](../te/README.md) | [தாய்](../th/README.md) | [துருக்கிஷ்](../tr/README.md) | [உக்ரைனியன்](../uk/README.md) | [உருது](../ur/README.md) | [வியட்நாமியம்](../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](./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)
+
+> **உள்ளூர் நகலை முன்னுரிமைப்படுத்துகிறீர்களா?**
+
+> இந்த தொடுப்பு 50+ மொழி மொழிபெயர்ப்புகளை உட்படுத்துகிறது, இது பதிவிறக்க அளவை பெரிதும் அதிகரிக்கிறது. மொழிபெயர்ப்புகளின்றி நகலை எடுக்க, இயல்புநிலை தேர்வு பயன்பாடு பயன்படுத்தவும்:
+> ```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'
+> ```
+> இது உங்கள் பாடத்தைக் நன்றாக முடிக்க தேவையான அனைத்தையும் விரைவான பதிவிறக்கத்துடன் தரும்.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### எங்கள் சமூகத்தில் இணையுங்கள்
+#### எங்கள் சமுதாயத்தில் சேருங்கள்
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-நாங்கள் ஒரு Discord "Learn with AI" தொடர் நடத்தி வருகிறோம், மேலும் விவரங்களுக்காக மற்றும் 18 - 30 செப்டம்பர், 2025 அன்று நடைபெறும் நிகழ்ச்சியில் கலந்து கொள்ள [Learn with AI Series](https://aka.ms/learnwithai/discord) என்ற இணைப்பில் சேரவும். Data Science க்கான GitHub Copilot பயன்படுத்துவதற்கான குறிப்புகள் மற்றும் முறைகள் கிடைக்கும்.
+எங்களிடம் AI இயங்கி கற்றுக்கொள்ளும் தொடர் நடைபெற்று கொண்டிருக்கிறது, மேலும் அறியவும் எங்களுக்கு இணையவும் [Learn with AI Series](https://aka.ms/learnwithai/discord) 18 - 30 செப்டம்பர், 2025 அன்று. நீங்கள் GitHub Copilot ஐ Data Science க்காக பயன்படுத்துவதற்கான குறிப்புகள் மற்றும்窍ක් கற்றுக்கொள்வீர்கள்.
 
-![AI உடன் கற்றல் தொடர்](../../translated_images/ta/3.9b58fd8d6c373c20.webp)
+![Lear with AI series](../../../../translated_images/ta/3.9b58fd8d6c373c20.webp)
 
-# துவக்கர்களுக்கான மெஷின் லெர்னிங் - ஒரு பாடத்திட்டம்
+# தொடக்கங்களுக்கான இயந்திர கற்றல் - ஒரு பாடத்திட்டம்
 
-> 🌍 உலக பண்பாட்டுகளின் மூலம் மெஷின் லெர்னிங்கை ஆராயும் பயணத்தில் உலகம் சுற்றுங்களாம் 🌍
+> 🌍 உலக கலாச்சாரங்களின் வழியாக இயந்திர கற்றலை ஆராய்ந்து உலகம் சுற்றி பயணம் செய்யுங்கள் 🌍
 
-Microsoft இல் உள்ள Cloud Advocates குழு 12 வாரங்கள், 26 பாடங்கள் கொண்ட முழு பாடத்திட்டமான **Machine Learning** ஐ வழங்க தொழில்நுட்பமாக மகிழ்ச்சியுடன் வழங்குகிறது. இந்த பாடத்திட்டத்தில், நீங்கள் பெரும்பாலும் Scikit-learn நூலகத்தை பயன்படுத்தி மற்றும் ஆழமான கற்றலைக் கைவிட்டு சில நேரங்களில் "classic machine learning" என்று அழைக்கப்படும் வகையைப் பற்றி கற்பீர்கள். மேலும் இதை எமது [AI for Beginners' பாடத்திட்டம்](https://aka.ms/ai4beginners) உடன் சேர்த்து ஆழமான கற்றலைப் பெறலாம். இதை எங்கள் ['Data Science for Beginners' பாடத்திட்டம்](https://aka.ms/ds4beginners) உடன் இணைத்து பயன்படுத்தவும்.
+Microsoft இல் Cloud Advocates, **Machine Learning** பற்றிய 12-வாரம், 26 பாடங்களின் பாடத்திட்டத்தை வழங்குவதில் மகிழ்ச்சியடைகிறது. இந்த பாடத்திட்டத்தில், **classic machine learning** என்று அழைக்கப்படக்கூடியதை உங்கள் கற்றுக்கொள்வீர்கள், இது சாதாரணமாக Scikit-learn நூலகத்தை பயன்படுத்துகின்றது மற்றும் நமது [AI for Beginners' பாடத்திட்டம்](https://aka.ms/ai4beginners) வழங்கும் ஆழமான கற்றலை தவிர்க்கிறது. இந்த பாடங்களை நமது ['Data Science for Beginners' பாடத்திட்டத்துடன்](https://aka.ms/ds4beginners) இணைத்துக் கொள்ளவும்!
 
-உலகின் பல பகுதிகளில் இருந்து எடுக்கப்பட்ட தரவுகளுடன் இந்த கம்ப்யூட்டிங் தொழில்நுட்பங்களை செலுத்தி பயணியுங்கள். ஒவ்வொரு பாடமும் முன்னும் பின்னும் க்விச், பாடத்தை முடிக்க படிப்படி வழிமுறைகள், ஒரு தீர்வு, ஒரு பணிப் பணி மற்றும் பலவற்றை கொண்டுள்ளது. எங்கள் திட்ட அடிப்படை பயிற்சி முறையால் நீங்கள் கட்டமைக்கும்போது கற்றுக் கொள்வீர்கள், இது புதிய திறமைகள் நிலைத்திருவதற்கு உதவுகிறது.
+உலகத்தின் பல பகுதிகளிலிருந்து தரவை பயன்படுத்தி இந்த classic தொழில்நுட்பங்களை செயல்படுத்தும் போது எங்களுடன் உலகத்தை சுற்றி பயணம் செய்யுங்கள். ஒவ்வொரு பாடமும் முன் மற்றும் பின் தேர்வுகளை, பாடத்தினை முடிக்க எழுத்து வழிமுறைகள், ஒரு தீர்வு, பணிப்பட்டி மற்றும் கூடுதல் அம்சங்களை கொண்டுள்ளது. எங்கள் திட்ட அடிப்படையுள்ள கல்வித்துறையால் நீங்கள் உருவாக்குவதற்காக கற்றல் செய்வீர்கள், இது புதிய திறன்களை நிலையானதாக மாற்றும் நம்பிக்கையான முறை.
 
-**✍️ எங்கள் எழுத்தாளர்களுக்கு மனமார்ந்த நன்றி** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu மற்றும் Amy Boyd
+**✍️ எங்கள் ஆசிரியர்களுக்கு உள்ளடக்கம்** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu மற்றும் Amy Boyd
 
-**🎨 நன்றி எமது வரைபடக் கலைஞர்களுக்கும்** Tomomi Imura, Dasani Madipalli, மற்றும் Jen Looper
+**🎨 எங்களின் வரைகலைஞர்களுக்கு நன்றி** Tomomi Imura, Dasani Madipalli மற்றும் Jen Looper
 
-**🙏 சிறப்பு நன்றி 🙏 எமது Microsoft Student Ambassador எழுத்தாளர்கள், மதிப்பாய்வு செய்பவர்கள் மற்றும் உள்ளடக்க பங்களிப்பாளர்களுக்கு**, குறிப்பாக Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, மற்றும் Snigdha Agarwal
+**🙏 எங்கள் Microsoft மாணவர் தூதர்கள், மதிப்பாய்வோர்கள் மற்றும் உள்ளடக்கச் சேர்ந்தவர்களுக்கு சிறப்பு நன்றி**, குறிப்பாக Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila மற்றும் Snigdha Agarwal
 
-**🤩 எமது R பாடங்களுக்காக Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, மற்றும் Vidushi Gupta ஆகியோருக்கு கூடுதல் நன்றிகள்!**
+**🤩 எங்கள் R பாடங்களுக்கு Microsoft மாணவர் தூதர்கள் Eric Wanjau, Jasleen Sondhi மற்றும் Vidushi Gupta அவர்களுக்கு கூடுதல் நன்றி!**
 
-# தொடங்குவது
+# துவக்கம்
 
 இந்த படிகளை பின்பற்றவும்:
-1. **கோப்பகத்தை Fork செய்யுங்கள்**: இந்த பக்கத்தின் மேல்-வலது மூலையில் உள்ள "Fork" பொத்தானை அழுத்தவும்.
-2. **கோப்பகத்தை Clone செய்யுங்கள்**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **கோப்பகம் தோக்கு**: இப்பக்கத்தின் மேல்-வலது பகுதியில் உள்ள "Fork" பொத்தானை அழுத்தவும்.
+2. **கோப்பகத்தை கிளோன் செய்**:  `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [இந்த பாடத்திட்டத்திற்கான கூடுதல் அனைத்து வளங்களையும் எமது Microsoft Learn சேகரிப்பில் காணவும்](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [இந்த பாடத்துக்கு தேவையான கூடுதல் வளங்களை எங்கள் Microsoft Learn சேகரிப்பில் காண்க](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **உதவி தேவைவுமா?** நிறுவல், அமைப்பு மற்றும் பாடங்களை இயக்கும் போது வரும் பொதுக் குறைகளை தீர்க்க எங்கள் [Troubleshooting Guide](TROUBLESHOOTING.md) ஐ பார்க்கவும்.
+> 🔧 **உதவி தேவையா?** நிறுவல், அமைப்பு மற்றும் பாடங்களை இயக்குவதில் பொதுவான பிரச்சனைகளுக்கு தீர்வுகளை காண, எங்கள் [திறம்பட வழிகாட்டி](TROUBLESHOOTING.md) ஐ பார்க்கவும்.
 
+**[மாணவர்கள்](https://aka.ms/student-page)**, இந்த பாடத்திட்டத்தை பயன்படுத்த, முழு ரெப்போவை உங்கள் சொந்த GitHub கணக்குக்கு கிளோன் செய்து உங்கள் தனிப்பட்டவாறோ அல்லது குழுவுடன் இணைந்து பணிகளை முடிக்கவும்:
 
-**[மாணவர்கள்](https://aka.ms/student-page)**, இந்த பாடத்திட்டத்தை பயன்படுத்த, முழு repo-வை உங்கள் GitHub கணக்கிற்கு fork செய்து தனியாக அல்லது குழுவாக பயிற்சிகளை முடிக்கவும்:
+- முன்-பாடத் தேர்வுடன் தொடங்கவும்.
+- பாடத்தை வாசித்து செயல்பாடுகளை முடிக்கவும், ஒவ்வொரு அறிவு சோதனைக்கும் இடையே நிறுத்தி சிந்திக்கவும்.
+- தீர்வு குறியீட்டை இயக்கிக்கொள்ளாமல் பாடங்களைப் புரிந்துகொண்டு திட்டங்களை உருவாக்க முயற்சிக்கவும்; இருப்பினும், அந்த குறியீடு /solution என்ற கோப்புறையில் ஒவ்வொரு திட்ட அடிப்படையுள்ள பாடத்திலும் கிடைக்கும்.
+- பின்-பாடத் தேர்வை எடுத்துக் கொள்ளவும்.
+- சவாலை முடிக்கவும்.
+- பணிப்பட்டியை முடிக்கவும்.
+- ஒரு பாடக் குழுவை முடித்துவிட்ட பிறகு, [பேச்சாளர் பலகையை](https://github.com/microsoft/ML-For-Beginners/discussions) சென்று "உயிருடன் கற்றுக்கொள்ளுங்கள்" மற்றும் தொடர்புடைய PAT மதிப்பீட்டுப் படிவத்தை பூர்த்தி செய்யவும். 'PAT' என்பது முன்னேற்ற மதிப்பீடு கருவி ஆகும், இது நீங்கள் உங்கள் கற்றலை மேலும் விரிவாக்க ஒரு படிவமாகும். மற்ற PAT களுக்கு நீங்கள் எதிர்வினையை தெரிவித்து நாம் ஒன்றாக கற்போம்.
 
-- முன்-பாடத் தேர்வுடன் துவங்குங்கள்.
-- வகுப்பை படித்து செயல்பாடுகளை முடிக்கவும்; ஒவ்வொரு அறிவு சோதனையிலும் நிறுத்தி குறித்த விஷயங்களை பரிசீலிக்கவும்.
-- தீர்வு குறியீட்டை இயக்குவதைக் காட்டிலும் பாடங்களை புரிந்து கொண்டு திட்டங்களை உருவாக்க முயற்சிக்கவும்; அதினாலும் அந்தக் குறியீடு ஒவ்வொரு திட்ட-ஆதார பாடத்திலும் உள்ள `/solution` கோப்புறைகளில் கிடைக்கிறது.
-- பாடம் முடிந்த பிறகு உள்ள க்விசை எடுத்துப் பாருங்கள்.
-- சவால்களை முடிக்கவும்.
-- ஒதுக்கப்பட்ட பணியை முடிக்கவும்.
-- ஒரு பாடக் குழுவை முடித்த பிறகு, [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) ஐப் பார்வையிட்டு உரிய PAT ரூப்ரிக் நிரப்புவது மூலம் "பொலிவாக கற்று கொள்ளுங்கள்". 'PAT' என்பது உங்கள் கற்றலை மேலும் ஊக்கப்படுத்த நீங்கள் நிரப்புகிற முன்னேற்ற மதிப்பீட்டு கருவி ஆகும். மற்ற PAT களுக்கு பிரதிசெய்தியைச் செலுத்துவதன்மூலம் நாமொன்றாக கற்றுக்கொள்ளலாம்.
+> மேலதிக படிப்புக்காக, இந்த [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) மொழிகள் மற்றும் கற்றல் பாதைகள் பரிந்துரைக்கப்படுகின்றன.
 
-> மேலும் படிப்பதற்காக, கீழ் உள்ள [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) மாட்யூல்கள் மற்றும் கற்றல் பாதைகளை பின்பற்ற பரிந்துரைக்கப்படுகின்றன.
-
-**ஆசிரியர்கள்**, இந்த பாடத்திட்டத்தை எப்படி பயன்படுத்துவது என்பதற்கு நாம் [சில பரிந்துரைகளை சேர்த்துள்ளோம்](for-teachers.md).
+**ஆசிரியர்கள்**, இந்த பாடத்திட்டத்தை எப்படி பயன்படுத்துவது என சில பரிந்துரைகள் நாம் [உள்ளடக்கமாக்கியுள்ளோம்](for-teachers.md).
 
 ---
 
-## வீடியோ நடைமுறை விளக்கங்கள்
+## வீடியோ நடைமுறை
 
-சில பாடங்கள் குறுகிய வடிவ வீடியோவாக கிடைக்கின்றன. இவைகளைப் பாடங்களில் நேரடியாக காணலாம், அல்லது கீழ் படத்தை கிளிக் செய்து [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) இல் காணலாம்.
+சில பாடங்கள் குறுகிய வடிவ ویடியோவாகக் கிடைக்கின்றன. இவை அனைத்தையும் பாடங்களில் நேரடி காணலாம் அல்லது [Microsoft Developer YouTube சேனலில் ML for Beginners தொகுப்பைக்](https://aka.ms/ml-beginners-videos) கிளிக் செய்து காணலாம்.
 
-[![ML for beginners பேனர்](../../translated_images/ta/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/ta/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## குழுவை சந்திக்கவும்
+## குழுவை சந்தியுங்கள்
 
-[![விளம்பர வீடியோ](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
 **GIF உருவாக்கியவர்** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 மேற்கண்ட படத்தை கிளிக் செய்து இந்த திட்டம் மற்றும் அதை உருவாக்கியவர்களின் பற்றிய வீடியோவை பாருங்கள்!
+> 🎥 மேலே உள்ள படத்தை கிளிக் செய்து இந்த திட்டம் மற்றும் அதனை உருவாக்கியவர்களைப் பற்றி காணவும்!
 
 ---
 
-## கல்விமுறை
+## கல்விப்பிரகாரம்
 
-இந்த பாடத்திட்டத்தை உருவாக்கும் போது நாம் இரண்டு கல்வி கொள்கைகளைத் தேர்ந்தெடுத்துள்ளோம்: இது நடைமுறையாகையான, திட்டம்-அடிப்படையிலான (project-based) ஆக இருக்க வேண்டும் மற்றும் அதில் அடிக்கடி க்விச்கள் (frequent quizzes) இருக்க வேண்டும். மேலும் இந்த பாடத்திட்டத்திற்கு ஒன்றிணைந்த ஒரு **தீம்** உள்ளது, இது ஒருங்கிணைப்புக்கு உதவும்.
+இந்த பாடத்திட்டத்தை உருவாக்கும் போது இரண்டு கல்வித் தத்துவங்களை தேர்ந்தெடுத்தோம்: அது செயல்பாடான **திட்ட அடிப்படையிலானது** மற்றும் அதில் **பதினொன்று மறுபடியும் தேர்வுகள்** அடங்கும். கூடுதலாக, இந்த பாடத்திட்டத்துக்கு ஒரே **தீம்** உள்ளது, அது ஒருங்கிணைப்பை வழங்குகிறது.
 
-உள்ளடக்கம் திட்டங்களோடு ஒத்துப்போகும்படி தொகுக்கப்பட்டால் மாணவர்களுக்கு அதிக ஈடுபாடு வழங்கும்படியும், கொள்கைகளின் நினைவாற்றலும் உயர்வதாக இருக்கும். மேலும் ஒரு வகுப்புக்கு முன் உள்ள விடுமுறை-விகிதம் குறைந்த-பரீட்சை மாணவரின் பாடம் கற்றுக்கொள்ளும் நோக்கத்தை உருவாக்கும்; வகுப்பின் பிறகு ஒரு இரண்டாவது க்விஸ் கூடுதல் நினைவாற்றலை உறுதிப்படுத்தும். இந்த பாடத்திட்டம் நெகிழ்வானதும் மகிழ்ச்சியானதுமானதாக வடிவமைக்கப்பட்டுள்ளது மற்றும் முழுவதையும் அல்லது ஒரு பகுதியைப் படித்து முடிக்கலாம். திட்டங்கள் சிறிது அளவில் துவங்கி 12 வார வட்டத்தின் முடிவில் வரையிலானவையாக progressively சிக்கலாகிறது. இந்த பாடத்திட்டம் மேலும் ML இன் இயல்பான பயன்பாடுகளின் மீது ஒரு பின்குறிப்பு(postscript) கொண்டுள்ளது, இது கூடுதல் கிரெடிட்டாக அல்லது கலந்துரையாடலுக்கான அடிப்படையாகப் பயன்படுத்தக்கூடியது.
+பாட உள்ளடக்கம் திட்டங்களுக்கு ஏற்ப அமைக்கப்பட்டதால், மாணவர்களுக்கு பாடம் ஈர்த்துக்கொள்ளக்கூடியது மற்றும் கருத்துக்களின் உறுதி அதிகரிக்கும். கூடுதலாக, வகுப்புக்கு முன்பான குறைந்த புள்ளி தேர்வு மாணவனுக்கு பாடத் தலைப்பின் நோக்கத்தை அமைக்கும், வகுப்புக்குப் பின் இரண்டாவது தேர்வு மேலதிக உறுதியை உறுதி செய்கிறது. இந்த பாடத்திட்டம் எளிமையானது, வேடிக்கையானது மற்றும் முழுமையாக அல்லது பகுதியாய் எடுத்துக்கொள்ளக் கூடியது. இந்த திட்டங்கள் சிறியது ஆரம்பித்து 12-வாரம் கடைசி வரை அதிகமாக சிக்கலாக மாறும். இந்த பாடத்திட்டத்தில் இயந்திரக் கற்றலின் உண்மையான உலகுப் பயன்பாடுகள் குறித்த ஒரு பின்னூட்டமும் உள்ளது, இது கூடுதல் தரம் அல்லது விவாதத்திற்கான அடிப்படையாக பயன்படுத்த முடியும்.
 
-> எங்கள் [நடத்தை விதிகள்](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), மற்றும் [Troubleshooting](TROUBLESHOOTING.md) கையேடுகளை காணவும். உங்கள் கட்டுமானமான கருத்துப்பின்னூட்டத்தைக் கிழிக்கிறோம்!
+> எங்கள் [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), மற்றும் [Troubleshooting](TROUBLESHOOTING.md) வழிகாட்டுதல்களை காணவும். உங்களது பயனுள்ள கருத்துக்களை வரவேற்கின்றோம்!
 
-## ஒவ்வொரு பாடத்திட்டமும் கொண்டவை
+## ஒவ்வொரு பாடத்திலும் உள்ளது
 
-- விருப்பமான ஸ்கெட்ச் நோட்
+- விருப்பமான அடையாளக் குறிப்பு
 - விருப்பமான கூடுதல் வீடியோ
-- வீடியோ நடைமுறை விளக்கம் (சில பாடங்களுக்கே மட்டும்)
-- [முன்-பாடம் தயாரிப்பு க்விஸ்](https://ff-quizzes.netlify.app/en/ml/)
-- எழுதப்பட்ட பாடம்
-- திட்டம்-அடிப்படையிலான பாடங்களுக்காக, திட்டத்தை எப்படி உருவாக்குவது என்பதற்கான படி-படி வழிகாட்டல்கள்
+- வீடியோ நடைமுறை (சில பாடங்களில் மட்டும்)
+- [முன்-வகுப்புத் தயாரிப்பு தேர்வு](https://ff-quizzes.netlify.app/en/ml/)
+- எழுத்துரு பாடம்
+- திட்ட அடிப்படையிலான பாடங்களுக்கு, திட்டத்தை கட்ட எவ்வாறு என்பதை படி படி வழிகாட்டல்கள்
 - அறிவு சோதனைகள்
-- ஒரு சவால்
-- கூடுதல் வாசிப்புகள்
-- பணி
-- [பாடம் பிறகு க்விஸ்](https://ff-quizzes.netlify.app/en/ml/)
-
-> **மொழிகள் பற்றிய ஒரு குறிப்பு**: இந்தக் பாடங்கள் முதன்மையாக Python-இல் எழுதப்பட்டவை, ஆனால் பல பாடங்களும் R இல் கிடைக்கின்றன. R பாடத்தை முடிக்க `/solution` கோப்புறைக்கு சென்று R பாடங்களைத் தேடுங்கள். அவை .rmd நீட்சியைக் கொண்டிருக்கும், இது ஒரு **R Markdown** கோப்பாகும், இது எளிதில் வரையறுக்கப்படுவது `code chunks` (R அல்லது பிற மொழிகள்) மற்றும் `YAML header` (PDF போன்ற வெளியீடுகளின் வடிவமைப்பை வழிகாட்டும்) ஆகியவற்றின் இணைப்பாக ஒரு `Markdown document` இல் உள்ளடக்கியதாகும். இதன் மூலம், உங்கள் குறியீட்டு பகுதி, அதன் வெளியீடு மற்றும் உங்கள் எண்ணங்களை ஒன்றிணைத்து Markdown இல் எழுதுவதன் மூலம் தொடர் ஆவணமாகக் கொண்டு வர முடியும். மேலும், R Markdown ஆவணங்கள் PDF, HTML அல்லது Word போன்ற வெளியீடு வடிவங்களுக்கு உருவாக்கப்படலாம்.
-
-> **க்விஸ் பற்றிய ஒரு குறிப்பு**: அனைத்து க்விஸ்களும் [Quiz App folder](../../quiz-app) இல் உள்ளன; மொத்தம் 52 க்விஸ்கள் உள்ளன, ஒவ்வொன்றிலும் மூன்று கேள்விகள். இவை பாடங்களில் இணைக்கப்பட்டுள்ளன, ஆனால் quiz app-ஐ உள்ளூரில் இயக்கிக் கொள்ளலாம்; உள்ளூரில் ஹோஸ்ட் செய்தல் அல்லது Azure-க்கு deploy செய்வதற்கான வழிமுறைகள் `quiz-app` கோப்புறையில் கொடுக்கப்பட்டுள்ளன.
-
-| பாடம் எண் |                             தலைப்பு                              |                   பாடம் குழு                   | கற்றல் நோக்கங்கள்                                                                                                             |                                                              இணைக்கப்பட்ட பாடம்                                                               |                        ஆசிரியர்                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                இயந்திரக் கற்றலுக்கு அறிமுகம்                |      [Introduction](1-Introduction/README.md)       | இயந்திரக் கற்றலின் அடிப்படை கருத்துக்களை கற்றுக்கொள்ளுங்கள்                                                                                |                                             [பாடம்](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                இயந்திரக் கற்றலின் வரலாறு                 |      [Introduction](1-Introduction/README.md)       | இந்த துறையின் அடிப்படையில் உள்ள வரலாற்றை கற்றுக்கொள்ளுங்கள்                                                                                         |                                            [பாடம்](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 நியாயம் மற்றும் இயந்திரக் கற்றல்                  |      [Introduction](1-Introduction/README.md)       | எம்எல் மாதிரிகளை உருவாக்கியதும் மற்றும் பயன்படுத்தியதும் மாணவர்கள் கவனிக்க வேண்டிய நியாயம் தொடர்பான முக்கிய தத்துவப் பிரச்சனைகள் எவை? |                                              [பாடம்](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                இயந்திரக் கற்றலுக்கான தொழில்நுட்பங்கள்                 |      [Introduction](1-Introduction/README.md)       | எம்எல் ஆராய்ச்சியாளர்கள் எம்எல் மாதிரிகளை உருவாக்க எந்த தொழில்நுட்பங்களைப் பயன்படுத்துகிறார்கள்?                                                                       |                                          [பாடம்](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   ரெக்ரெஷன் அறிமுகம்                   |        [Regression](2-Regression/README.md)         | ரெக்ரெஷன் மாதிரிகளுக்காக Python மற்றும் Scikit-learn உடன் துவங்குங்கள்                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                வட அமெரிக்க பூசணிக்காய் விலைகள் 🎃                |        [Regression](2-Regression/README.md)         | எம்எலுக்கான தயாரிப்பாக தரவை காட்சியிடவும் மற்றும் சுத்தம் செய்யவும்                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                வட அமெரிக்க பூசணிக்காய் விலைகள் 🎃                |        [Regression](2-Regression/README.md)         | லினியர் மற்றும் பாலினோமியல் ரெக்ரெஷன் மாதிரிகளை உருவாக்கவும்                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                வட அமெரிக்க பூசணிக்காய் விலைகள் 🎃                |        [Regression](2-Regression/README.md)         | ஒரு லொஜிஸ்டிக் ரெக்ரெஷன் மாதிரியை உருவாக்கவும்                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          ஒரு வலை பயன்பாடு 🔌                          |           [Web App](3-Web-App/README.md)            | பயிற்சி பெற்ற மாதிரியை பயன்படுத்த ஒரு வலைப் பயன்பாட்டை உருவாக்கவும்                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 வகைப்படுத்தலுக்கான அறிமுகம்                 |    [Classification](4-Classification/README.md)     | உங்கள் தரவுகளை சுத்தம் செய்து, தயாரித்து, காட்சியிடுங்கள்; வகைப்படுத்தலுக்கான அறிமுகம்                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜             |    [Classification](4-Classification/README.md)     | வகைப்பவர்(கிளாஸிபையர்) அறிமுகம்                                                                                                     | [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       |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜             |    [Classification](4-Classification/README.md)     | மேலும் கிளாஸிபையர்கள்                                                                                                                | [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       |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜             |    [Classification](4-Classification/README.md)     | உங்கள் மாதிரியைப் பயன்படுத்தி ஒரு பரிந்துரை செய்யும் வலைப் பயன்பாட்டை உருவாக்கவும்                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   கிளஸ்டரிங் அறிமுகம்                   |        [Clustering](5-Clustering/README.md)         | உங்கள் தரவுகளை சுத்தம் செய்து, தயாரித்து, காட்சியிடுங்கள்; கிளஸ்டரிங்கிற்கான அறிமுகம்                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              நைஜீரிய இசை ருசிகளை ஆராய்தல் 🎧              |        [Clustering](5-Clustering/README.md)         | K-Means கிளஸ்டரிங் முறையை ஆராயுங்கள்                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        இயல்பான மொழி செயலாக்கத்திற்கான அறிமுகம் ☕️         |   [Natural language processing](6-NLP/README.md)    | ஒரு எளிய பாட்டை உருவாக்குவதன் மூலம் NLP பற்றிய அடிப்படைகளை கற்றுக்கொள்ளுங்கள்                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      பொதுவான NLP பணிகள் ☕️                      |   [Natural language processing](6-NLP/README.md)    | மொழி அமைப்புகளுடன் வேலை செய்வதில் தேவையான பொதுவான பணிகளைப் புரிந்து கொண்டு NLP அறிவை ஆழப்படுத்துங்கள்                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             மொழிபெயர்ப்பு மற்றும் உணர்வு பகுப்பாய்வு ♥️              |   [Natural language processing](6-NLP/README.md)    | ஜேன் ஆஸ்டினுடன் மொழிபெயர்ப்பு மற்றும் உணர்வு பகுப்பாய்வு                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  ரோமான்டிக் ஹோட்டல்கள் ஐரோப்பாவின் ♥️                  |   [Natural language processing](6-NLP/README.md)    | ஹோட்டல் மதிப்புரைகளுடன் உணர்வு பகுப்பாய்வு 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  ரோமான்டிக் ஹோட்டல்கள் ஐரோப்பாவின் ♥️                  |   [Natural language processing](6-NLP/README.md)    | ஹோட்டல் மதிப்புரைகளுடன் உணர்வு பகுப்பாய்வு 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            கால வரிசை முன்னறிவிப்பு அறிமுகம்             |        [Time series](7-TimeSeries/README.md)        | கால வரிசை முன்னறிவிப்பிற்கு அறிமுகம்                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ உலக மின்சாரம் பயன்பாடு ⚡️ - ARIMA உடன் கால வரிசை முன்னறிவிப்பு |        [Time series](7-TimeSeries/README.md)        | ARIMA மூலம் கால வரிசை முன்னறிவிப்பு                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ உலக மின்சாரம் பயன்பாடு ⚡️ - SVR உடன் கால வரிசை முன்னறிவிப்பு  |        [Time series](7-TimeSeries/README.md)        | Support Vector Regressor மூலம் கால வரிசை முன்னறிவிப்பு                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             ரீஇன்போர்ஸ்மெண்ட் லெர்னிங்கின் அறிமுகம்             | [Reinforcement learning](8-Reinforcement/README.md) | Q-Learning உடன் ரீஇன்போர்ஸ்மெண்ட் லெர்னிங்கின் அறிமுகம்                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 பீட்டரை நரியைத் தவிர்க்க உதவுங்கள்! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | ரீஇன்போர்ஸ்மெண்ட் லெர்னிங் ஜிம்                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  பின்குறிப்பு   |            உலக உண்மையான எம்எல் சூழ்நிலைகள் மற்றும் பயன்பாடுகள்            |      [ML in the Wild](9-Real-World/README.md)       | பாரம்பரிய எம்எலின் சுவாரசியமான மற்றும் வெளிப்படுத்தும் நடைமுறை பயன்பாடுகள்                                                               |                                             [பாடம்](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  பின்குறிப்பு   |            RAI டாஷ்போர்டைப் பயன்படுத்தி எம்எலில் மாதிரி டிபக்கிங்          |      [ML in the Wild](9-Real-World/README.md)       | Responsible AI டாஷ்போர்டு கூறுகளைப் பயன்படுத்தி இயந்திரக் கற்றலில் மாதிரி டிபக்கிங்                                                              |                                             [பாடம்](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [இந்த பாடத்திற்கான மேலும் அனைத்து வளங்களையும் எங்கள் Microsoft Learn சேமிப்பகத்தில் காண்க](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- சவால்
+- கூடுதல் வாசிப்பு
+- பணிப்பட்டை
+- [பின்-வகுப்புத் தேர்வு](https://ff-quizzes.netlify.app/en/ml/)
+
+> **மொழிகள் குறித்த ஒரு குறிப்புரை**: இந்த பாடங்கள் முதன்மையாக Python இல் எழுதப்பட்டுள்ளன, ஆனால் பல R ல் மேலும் கிடைக்கின்றன. R பாடத்தை முடிக்க, `/solution` கோப்புறை நோக்கி R பாடங்களைத் தேடி காணவும். அவை `.rmd` நீட்சியைக் கொண்டுள்ளன, இது ஒரு **R மார்க்டவுன்** கோப்பாகும் மற்றும் இது `code chunks` (R அல்லது பிற மொழிகளுக்கு) மற்றும் `YAML header` (PDF போன்ற வெளியீடுகளை வடிவமைக்க வழிகாட்டுகிறது) இணைந்த படிவமாகும். இதனால், இது தரவு அறிவியலுக்கான சிறந்த எழுத்தாக்க வடிவமைப்பாக அமைகிறது, ஏனெனில் உங்கள் குறியீடு, அதன் வெளியீடு மற்றும் உங்கள் எண்ணங்களைMarkdown இல் எழுத அனுமதிக்கிறது. மேலும், R Markdown கோப்புகள் PDF, HTML, அல்லது Word போன்ற வெளியீட்டு வடிவங்களாக மாற்றப்பட முடியும்.
+> **க்விஸ் குறித்து ஒரு குறிப்பு**: அனைத்து க்விஸ்களும் [Quiz App கோப்புறை](../../quiz-app) இல் உள்ளன, ஒவ்வொன்றிலும் மூன்று கேள்விகள் கொண்ட மொத்தம் 52 க்விஸ்கள் உள்ளன. அவை பாடங்களுக்குள் இணைக்கப்பட்டுள்ளன, ஆனால் க்விஸ் செயலி உள்ளூர் முறையில் இயக்கப்படலாம்; உள்ளூர் முறையில் ஹோஸ்ட் செய்ய அல்லது Azure-க்கு அனுப்ப `quiz-app` கோப்புறையில் உள்ள வழிமுறைகளை பின்பற்றவும்.
+
+| பாட எண் |                             தலைப்பு                             |                   பாடக் குழு                    | கற்றல் குறிக்கோள்கள்                                                                                                             |                                                              இணைக்கப்பட்ட பாடம்                                                               |                        ஆசிரியர்                        |
+| :------: | :------------------------------------------------------------: | :----------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|    01    |                இயந்திரக் கற்றலில் அறிமுகம்                      |      [Introduction](1-Introduction/README.md)       | இயந்திரக் கற்றலின் அடிப்படை கருத்துக்களை கற்றுக்கொள்                                                                              |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       முஹம்மது                       |
+|    02    |                இயந்திரக் கற்றலின் வரலாறு                        |      [Introduction](1-Introduction/README.md)       | இந்த துறையின் பின்னணியினை கற்றுக்கொள்                                                                                         |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     ஜென் மற்றும் ஏமி                      |
+|    03    |                 நியாயத்தன்மை மற்றும் இயந்திரக் கற்றல்           |      [Introduction](1-Introduction/README.md)       | எஃது முக்கிய தத்துவமானச் சிக்கல்களைப் படிக்கும் மாணவர்கள் எல்.எம் மாதிரிகளை உருவாக்கி பயன்படுத்தும்போது பரிசீலிக்க வேண்டும்?       |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        தோமோமி                        |
+|    04    |                இயந்திரக் கற்றலுக்கான தொழில்நுட்பங்கள்             |      [Introduction](1-Introduction/README.md)       | எது தொழில்நுட்பங்களை எல்.எம் ஆராய்ச்சியாளர்கள் எல்.எம் மாதிரிகளை உருவாக்க பயன்படுத்துகிறார்கள்?                                   |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    கிறிஸ் மற்றும் ஜென்                     |
+|    05    |                   மறுமொழிகாட்டலில் அறிமுகம்                     |        [Regression](2-Regression/README.md)         | மறுமொழிகாட்டல் மாதிரிகளுக்கான Python மற்றும் Scikit-learn பயன்படுத்த ஆரம்பி                                                   |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      ஜென் • எரிக் வான்ஜவ்       |
+|    06    |                வட அமெரிக்க பரங்கிக்காய் விலை 🎃               |        [Regression](2-Regression/README.md)         | எல்.எம் முன்னேற்பாட்டுக்கு தரவை காண்பிக்கவும் சுத்தம் செய்யவும்                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      ஜென் • எரிக் வான்ஜவ்       |
+|    07    |                வட அமெரிக்க பரங்கிக்காய் விலை 🎃               |        [Regression](2-Regression/README.md)         | நேரியல் மற்றும் பகுப்பாய்வுக் குட்போன்ற மறுமொழிகாட்டல் மாதிரிகளை உருவாக்கு                                                      |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      ஜென் மற்றும் ட்மிட்ரி • எரிக் வான்ஜவ்       |
+|    08    |                வட அமெரிக்க பரங்கிக்காய் விலை 🎃               |        [Regression](2-Regression/README.md)         | ஒரு லாஜிஸ்டிக் மறுமொழி மாதிரியை உருவாக்கு                                                                                     |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      ஜென் • எரிக் வான்ஜவ்       |
+|    09    |                          ஒரு வலை செயலி 🔌                      |           [Web App](3-Web-App/README.md)            | பயிற்சி பெற்ற மாதிரியை பயன்படுத்த ஒரு வலை செயலியை உருவாக்கு                                                                  |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         ஜென்                          |
+|    10    |                 வகைப்பாட்டிற்கான அறிமுகம்                       |    [Classification](4-Classification/README.md)     | தரவை சுத்தம் செய்ய, தயாராக்கு, மற்றும் காண்பி; வகைப்பாட்டிற்கு அறிமுகம்                                                          | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | ஜென் மற்றும் காசி • எரிக் வான்ஜவ் |
+|    11    |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜           |    [Classification](4-Classification/README.md)     | வகைப்பாட்டாளர்களின் அறிமுகம்                                                                                                   | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | ஜென் மற்றும் காசி • எரிக் வான்ஜவ் |
+|    12    |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜           |    [Classification](4-Classification/README.md)     | மேலும் வகைப்பாட்டாளர்கள்                                                                                                        | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | ஜென் மற்றும் காசி • எரிக் வான்ஜவ் |
+|    13    |             சுவையான ஆசிய மற்றும் இந்திய உணவுகள் 🍜           |    [Classification](4-Classification/README.md)     | உங்கள் மாதிரியை பயன்படுத்தி பரிந்துரை வலை செயலியை உருவாக்கு                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         ஜென்                          |
+|    14    |                   தொகுப்பாக்கலுக்கான அறிமுகம்                   |        [Clustering](5-Clustering/README.md)         | தரவை சுத்தம் செய்ய, தயாராக்கு மற்றும் காண்பி; தொகுப்பாக்கலுக்கான அறிமுகம்                                                        |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      ஜென் • எரிக் வான்ஜவ்       |
+|    15    |              நைஜீரிய இசை சுவைகளை ஆராய்தல் 🎧                 |        [Clustering](5-Clustering/README.md)         | K-அர்த்தங்கள் தொகுப்பாக்கல் முறையை ஆராய்தல்                                                                                    |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      ஜென் • எரிக் வான்ஜவ்       |
+|    16    |        இயற்கை மொழி செயலாக்கத்திற்கு அறிமுகம் ☕️                 |   [Natural language processing](6-NLP/README.md)    | எளிய ஒரு பாட்டைப் பயன்படுத்தி NLP அடிப்படைகளை கற்று                                                                                   |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       ஸ்டீபன்                        |
+|    17    |                      பொதுவான NLP பணிகள் ☕️                    |   [Natural language processing](6-NLP/README.md)    | மொழி கட்டமைப்புகளுடன் வேலை செய்யும் பொழுது தேவையான பொதுவான பணிகளைப் புரிந்து உங்கள் NLP அறிவை விரிவு செய்யுங்கள்                        |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       ஸ்டீபன்                        |
+|    18    |             மொழி மாற்றம் மற்றும் உணர்வு பகுப்பாய்வு ♥️          |   [Natural language processing](6-NLP/README.md)    | ஜேன் ஆஸ்டன் உடன் மொழி மாற்றம் மற்றும் உணர்வு பகுப்பாய்வு                                                                        |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       ஸ்டீபன்                        |
+|    19    |                  ஐரோப்பிய காதல் ஹோட்டல்கள் ♥️                  |   [Natural language processing](6-NLP/README.md)    | ஹோட்டல் மதிப்பாய்வுகளுடன் உணர்வு பகுப்பாய்வு 1                                                                                   |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       ஸ்டீபன்                        |
+|    20    |                  ஐரோப்பிய காதல் ஹோட்டல்கள் ♥️                  |   [Natural language processing](6-NLP/README.md)    | ஹோட்டல் மதிப்பாய்வுகளுடன் உணர்வு பகுப்பாய்வு 2                                                                                   |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       ஸ்டீபன்                        |
+|    21    |            கால வரிசை முன்னறிவிப்பிற்கான அறிமுகம்               |        [Time series](7-TimeSeries/README.md)        | கால வரிசை முன்னறிவிப்பிற்கான அறிமுகம்                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      பிரான்செஸ்கா                       |
+|    22    | ⚡️ உலக சக்தி பயன்பாடு ⚡️ - ARIMA உடன் கால வரிசை முன்னறிதல்   |        [Time series](7-TimeSeries/README.md)        | ARIMA உடன் கால வரிசை முன்னறிதல்                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      பிரான்செஸ்கா                       |
+|    23    |  ⚡️ உலக சக்தி பயன்பாடு ⚡️ - SVR உடன் கால வரிசை முன்னறிதல்    |        [Time series](7-TimeSeries/README.md)        | ஆதரவு வெக்டர் ரெக்ரெஷன் மூலம் கால வரிசை முன்னறிதல்                                                                             |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       அனிர்பன்                        |
+|    24    |             உறுதியான கற்றலுக்கான அறிமுகம்                        | [Reinforcement learning](8-Reinforcement/README.md) | Q கற்றலுடன் உறுதியான கற்றலுக்கான அறிமுகம்                                                                                      |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        ட்மிட்ரி                        |
+|    25    |                 பீட்டர் ஆட்டை தவிர்க்க உதவுங்கள்! 🐺             | [Reinforcement learning](8-Reinforcement/README.md) | உறுதியான கற்றல் ஜிம்                                                                                                          |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        ட்மிட்ரி                        |
+|  பிறகு பதிவு  |            உண்மையான உலக இயந்திரக் கற்றல் நிகழ்வுகள் மற்றும் பயன்பாடுகள்         |      [ML in the Wild](9-Real-World/README.md)       | பாரம்பரிய இயந்திரக் கற்றலின் சுவையான மற்றும் வெளிப்படுத்தும் உண்மையான உலக பயன்பாடுகள்                                            |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         குழு                         |
+|  பிறகு பதிவு  |            RAI டாஷ்போர்டைப் பயன்படுத்தி ML மாதிரிகளை பிழைதிருத்தல்           |      [ML in the Wild](9-Real-World/README.md)       | பொறுப்பு AI டாஷ்போர்டு கூறுபடுகளுடன் இயந்திரக் கற்றலில் மாதிரி பிழைதிருத்தல்                                                            |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         ரூத் யாகுபு                       |
+
+> [இந்த பாடத்துக்கான அனைத்து கூடுதல் வளங்களையும் Microsoft Learn சேகரிப்பில் காண்க](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## ஆஃப்லைன் அணுகல்
 
-இந்த ஆவணத்தை நீங்கள் [Docsify](https://docsify.js.org/#/) ஐ பயன்படுத்தி ஆஃப்லைனில் இயக்கலாம். இந்த repo-ஐ fork செய்து, உங்கள் உள்ளூர் கணினியில் [Docsify ஐ நிறுவவும்](https://docsify.js.org/#/quickstart), பின்னர் இந்த repo-வின் ரூட் கோப்பகத்தில் `docsify serve` என தட்டச்சு செய்யவும். வலைத்தளம் உங்கள் localhost இல் போர்டு 3000 இல் வழங்கப்படும்: `localhost:3000`.
+[Docsify](https://docsify.js.org/#/) ஐப் பயன்படுத்தி நீங்கள் இந்த ஆவணத்தைக் ஆஃப்லைனில் இயக்கலாம். இந்த ரெப்போவை ஃபோர்க் செய்து, உங்கள் உள்ளூர் கணினியில் [Docsify ஐ நிறுவவும்](https://docsify.js.org/#/quickstart), பின்னர் இந்த ரெப்போவில் ரூட் கோப்புறையில் `docsify serve` என தட்டச்சு செய்யவும். இந்த வலைத்தளம் உங்கள் உள்ளூர் கணினியில் 3000 என்ற போர்ட்டில் சேவை செய்யப்படும்: `localhost:3000`.
 
 ## PDFகள்
 
-இத்திட்டத்தின் விவரக்கூட்டத்தில் இணைப்புகளுடன் உள்ள pdf-ஐ [இங்கே](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) காண்க.
+இதற்கு இணைப்புகளுடன் கூடிய பாடத்திட்டத்தின் PDF-ஐ [இங்கிருந்து](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) காணவும்.
 
 
-## 🎒 பிற பாடங்கள் 
+## 🎒 பிற பாடங்கள்
 
-எங்கள் குழு மற்ற பாடங்களையும் உருவாக்குகிறது! பார்க்க:
+எங்கள் குழு பிற பாடங்களை உருவாக்குகிறது! பாருங்கள்:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j ஆரம்பத்திற்கான](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 ஆரம்பத்திற்கான](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
-[![AZD ஆரம்பத்திற்கான](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 ஆரம்பத்திற்கான](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 ஆரம்பத்திற்கான](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 ஆரம்பத்திற்கான](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)
 
 ---
  
-### உருவாக்குநர் AI தொடர்
-[![Generative AI ஆரம்பத்திற்கான](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)
-[![உற்பத்தி செயற்கை நுண்ணறிவு (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)
-[![உற்பத்தி செயற்கை நுண்ணறிவு (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 Series
+[![அறிமுகத்திற்கான உருவாக்கும் ஏ.ஐ](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)
+[![உருவாக்கும் ஏ.ஐ (.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)
+[![உருவாக்கும் ஏ.ஐ (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)
+[![உருவாக்கும் ஏ.ஐ (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)
 
 ---
  
-### முக்கியக் கற்றல்
-[![ஆரம்பிகளுக்கான இயந்திரக் கற்றல்](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)
-[![ஆரம்பிகளுக்கான தரவு அறிவியல்](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)
-[![ஆரம்பிகளுக்கான செயற்கை நுண்ணறிவு](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)
-[![ஆரம்பிகளுக்கான சைபர் பாதுகாப்பு](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)
-[![ஆரம்பிகளுக்கான வலை மேம்பாடு](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)
-[![ஆரம்பிகளுக்கான 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)
-[![ஆரம்பிகளுக்கான XR மேம்பாடு](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)
+### அடிப்படைக் கற்றல்
+[![ஏ.ஐ தொடக்கத்திற்கான எம்.எல்](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)
+[![தரவு அறிவியல் தொடக்கத்திற்கான](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)
+[![ஏ.ஐ தொடக்கத்திற்கான](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)
+[![சைபர் பாதுகாப்பு தொடக்கத்திற்கான](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)
+[![வலை உருவாக்கல் தொடக்கத்திற்கான](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)
+[![இன்டர்நெட் ஆப் திங்ஸ் தொடக்கத்திற்கான](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)
+[![எக்ஸ்ஆர் வளர்ச்சி தொடக்கத்திற்கான](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 தொடர்
-[![AI இணை நிரலாக்கத்திற்கான 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-க்கான 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 சாகசம்](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)
+### கோபைலட் தொடர்
+[![ஏ.ஐ இணைந்த கோபைலட் தொடர்பான](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 கோபைலட் தொடர்பான](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)
+[![கோபைலட் சாகசம்](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 -->
 
 ## உதவி பெறுதல்
 
-AI செயலிகளை உருவாக்குவதில் சிக்கி போய்க் கிடக்கும்போது அல்லது ஏதேனும் கேள்விகள் இருந்தால், MCP குறித்து நடத்தப்படும் விவாதங்களில் மற்ற கற்றலாளர்கள் மற்றும் அனுபவமிக்க டெவலப்பர்களுடன் இணைந்து கலந்துகொள்ளுங்கள். இது கேள்விகள் வரவேற்கப்படும் மற்றும் அறிவு சுதந்திரமாக பகிரப்படும் ஒரு ஆதரவு சமூகமாகும்.
+உங்களுக்கு ஏ.ஐ செயலிகளை கட்டமைப்பதில் சிக்கல் அல்லது கேள்விகள் இருந்தால். MCP பற்றி விவாதங்கள் நடைபெறும் பிற கற்றலாளர்களும் அனுபவம் வாய்ந்த அபிவிருத்தியாளர்களும் கூடிய சமுதாயத்தில் சேருங்கள். கேள்விகள் வரவேற்பு பெறும் மற்றும் அறிவு சுதந்திரமாக பகிரப்படும் ஒரு ஆதரவான சமூகமாகும்.
 
-[![Microsoft Foundry டிஸ்கார்ட்](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)
 
-If you have product feedback or errors while building visit:
+தயாரிப்பு கருத்து அல்லது கட்டமைக்கும் போது பிழைகள் இருந்தால் ביקரம்:
 
-[![Microsoft Foundry டெவலப்பர் மன்றம்](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)
 
 ---
 
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-மறுப்பு:
-இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) மூலம் மொழிபெயர்க்கப்பட்டுள்ளது. நாங்கள் துல்லியத்திற்காக முயற்சி செய்கிறோம் என்றாலும், தானியங்கி மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறான பொருள் விளக்கங்கள் இருக்கலாம் என்பதை கவனத்தில் கொள்ளவும். சொந்த மொழியில் உள்ள மூல ஆவணம் அதிகாரபூர்வ மூலமாகக் கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்காக, தொழில்முறை மனித மொழிபெயர்ப்பாளர்களை அணுக பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பின் பயன்பாட்டால் ஏற்படும் எந்தவொரு புரிதல் தவறுகள் அல்லது தவறான விளக்கங்களுக்காக நாங்கள் பொறுப்பேற்கமாட்டோம்.
+**கவனிக்கை**:  
+இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) பயன்படுத்தி மொழிபெயர்க்கப்பட்டுள்ளது. நாங்கள் துல்லியத்தை உறுதிப்படுத்த முயல்வதினாலும், தானாக வழங்கப்படும் மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறுகள் இருக்கக்கூடும் என்பதை தயவுசெய்து அறிந்திருங்கள். சொந்த மொழியில் உள்ள அசல் ஆவணம் அதிகாரப்பூர்வ மூலமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்நுட்பமான மனித மொழிபெயர்ப்பாரை அணுக பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பின் பயன்பாட்டால் ஏற்படக்கூடிய எந்த தவறான புரிதலுக்கும் அல்லது தவறான விளக்கங்களுக்கும் நாம் பொறுப்பேற்கமுடியாது.
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