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diff --git a/translations/id/README.md b/translations/id/README.md
index 8fa63df04..66352bbd7 100644
--- a/translations/id/README.md
+++ b/translations/id/README.md
@@ -1,21 +1,21 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T12:39:12+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T14:08:10+00:00",
   "source_file": "README.md",
   "language_code": "id"
 }
 -->
-[![Lisensi GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![Kontributor GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![Isu GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![Permintaan tarik GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![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)
 
-[![Pemirsa GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![Forks GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![Bintang GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 ### 🌐 Dukungan Multi-Bahasa
 
@@ -23,147 +23,155 @@ CO_OP_TRANSLATOR_METADATA:
 
 <!-- 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](./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)
+
+> **Lebih suka Mengkloning Secara Lokal?**
+
+> Repositori ini mencakup lebih dari 50 terjemahan bahasa yang secara signifikan meningkatkan ukuran unduhan. Untuk mengkloning tanpa terjemahan, gunakan 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'
+> ```
+> Ini memberi Anda semua yang Anda butuhkan untuk menyelesaikan kursus dengan unduhan yang jauh lebih cepat.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### Bergabung dengan Komunitas Kami
+#### Bergabunglah dengan Komunitas Kami
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Kami memiliki rangkaian acara Learn with AI di Discord yang sedang berlangsung, pelajari lebih lanjut dan bergabung bersama kami di [Learn with AI Series](https://aka.ms/learnwithai/discord) dari 18 - 30 September, 2025. Anda akan mendapatkan tips dan trik menggunakan GitHub Copilot untuk Data Science.
+Kami memiliki seri belajar dengan AI di Discord, pelajari lebih lanjut dan bergabunglah bersama kami di [Learn with AI Series](https://aka.ms/learnwithai/discord) dari 18 - 30 September, 2025. Anda akan mendapatkan tips dan trik menggunakan GitHub Copilot untuk Data Science.
 
-![Rangkaian Learn with AI](../../translated_images/id/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/id/3.9b58fd8d6c373c20.webp)
 
-# Pembelajaran Mesin untuk Pemula - Sebuah Kurikulum
+# Machine Learning untuk Pemula - Kurikulum
 
-> 🌍 Jelajahi dunia sambil kita mengeksplorasi Pembelajaran Mesin melalui budaya-budaya dunia 🌍
+> 🌍 Jelajahi dunia saat kita mengeksplorasi Machine Learning melalui budaya dunia 🌍
 
-Cloud Advocates di Microsoft dengan senang hati menawarkan kurikulum 12-minggu, 26-pelajaran yang membahas **Pembelajaran Mesin**. Dalam kurikulum ini, Anda akan belajar tentang apa yang kadang disebut **pembelajaran mesin klasik**, menggunakan terutama Scikit-learn sebagai pustaka dan menghindari deep learning, yang dibahas dalam [kurikulum 'AI for Beginners'](https://aka.ms/ai4beginners). Padukan pelajaran-pelajaran ini dengan juga mengikuti ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners)!
+Cloud Advocates di Microsoft dengan bangga menawarkan kurikulum 12-minggu, 26-pelajaran yang membahas **Machine Learning**. Dalam kurikulum ini, Anda akan belajar tentang apa yang kadang disebut **machine learning klasik**, menggunakan terutama perpustakaan Scikit-learn dan menghindari pembelajaran mendalam, yang dibahas dalam [kurikulum AI untuk Pemula](https://aka.ms/ai4beginners). Padu padankan pelajaran ini dengan kurikulum ['Data Science untuk Pemula'](https://aka.ms/ds4beginners), juga!
 
-Jelajahi bersama kami ke berbagai penjuru dunia saat kami menerapkan teknik klasik ini pada data dari berbagai wilayah. Setiap pelajaran mencakup kuis sebelum dan sesudah pelajaran, instruksi tertulis untuk menyelesaikan pelajaran, solusi, tugas, dan lainnya. Pedagogi berbasis proyek kami memungkinkan Anda belajar sambil membangun, sebuah cara terbukti agar keterampilan baru 'melekat'.
+Jelajahi bersama kami ke berbagai belahan dunia saat kami menerapkan teknik klasik ini pada data dari berbagai wilayah di dunia. Setiap pelajaran mencakup kuis sebelum dan sesudah pelajaran, instruksi tertulis untuk menyelesaikan pelajaran, solusi, tugas, dan lainnya. Pendekatan berbasis proyek memungkinkan Anda belajar sambil membangun, cara efektif agar keterampilan baru 'bersemangat'.
 
-**✍️ Terima kasih banyak kepada para penulis kami** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu dan Amy Boyd
+**✍️ Terima kasih sebesar-besarnya kepada penulis kami** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu dan Amy Boyd
 
 **🎨 Terima kasih juga kepada ilustrator kami** Tomomi Imura, Dasani Madipalli, dan Jen Looper
 
-**🙏 Terima kasih khusus 🙏 kepada para Microsoft Student Ambassador yang menjadi penulis, pengulas, dan kontributor konten kami**, terutama Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, dan Snigdha Agarwal
+**🙏 Terima kasih khusus 🙏 kepada penulis, pengulas, dan kontributor konten Microsoft Student Ambassador kami**, khususnya Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, dan Snigdha Agarwal
 
 **🤩 Terima kasih ekstra kepada Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, dan Vidushi Gupta untuk pelajaran R kami!**
 
 # Memulai
 
 Ikuti langkah-langkah ini:
-1. **Fork Repository**: Klik tombol "Fork" di pojok kanan atas halaman ini.
-2. **Clone Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **Fork Repositori**: Klik tombol "Fork" di sudut kanan atas halaman ini.
+2. **Clone Repositori**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [temukan semua sumber tambahan untuk kursus ini di koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [temukan semua sumber daya tambahan untuk kursus ini di koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Perlu bantuan?** Periksa [Panduan Pemecahan Masalah](TROUBLESHOOTING.md) kami untuk solusi atas masalah umum terkait instalasi, pengaturan, dan menjalankan pelajaran.
+> 🔧 **Butuh bantuan?** Periksa [Panduan Pemecahan Masalah](TROUBLESHOOTING.md) kami untuk solusi masalah umum dengan pemasangan, penyiapan, dan menjalankan pelajaran.
 
 
-**[Siswa](https://aka.ms/student-page)**, untuk menggunakan kurikulum ini, fork seluruh repo ke akun GitHub Anda sendiri dan selesaikan latihan sendiri atau bersama kelompok:
+**[Siswa](https://aka.ms/student-page)**, untuk menggunakan kurikulum ini, fork seluruh repo ke akun GitHub Anda sendiri dan selesaikan latihan secara mandiri atau dalam kelompok:
 
-- Mulailah dengan kuis pra-ceramah.
-- Baca materi dan selesaikan aktivitas, jeda dan renungkan pada setiap pemeriksaan pengetahuan.
-- Cobalah membuat proyek dengan memahami pelajarannya alih-alih langsung menjalankan kode solusi; namun kode tersebut tersedia di folder `/solution` di setiap pelajaran berorientasi proyek.
-- Ikuti kuis pasca-ceramah.
+- Mulai dengan kuis pemanasan sebelum kuliah.
+- Baca kuliah dan selesaikan aktivitas, berhenti dan renungkan setiap pemeriksaan pengetahuan.
+- Coba buat proyek dengan memahami pelajaran daripada langsung menjalankan kode solusi; tetapi kode tersebut tersedia di folder `/solution` pada setiap pelajaran berbasis proyek.
+- Ikuti kuis setelah kuliah.
 - Selesaikan tantangan.
 - Selesaikan tugas.
-- Setelah menyelesaikan kelompok pelajaran, kunjungi [Papan Diskusi](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar dengan suara" dengan mengisi rubrik PAT yang sesuai. 'PAT' adalah Progress Assessment Tool yang merupakan rubrik yang Anda isi untuk memperdalam pembelajaran. Anda juga dapat bereaksi terhadap PAT orang lain supaya kita dapat belajar bersama.
+- Setelah menyelesaikan satu kelompok pelajaran, kunjungi [Papan Diskusi](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar secara terbuka" dengan mengisi rubrik PAT yang sesuai. 'PAT' adalah Alat Penilaian Kemajuan yang merupakan rubrik yang Anda isi untuk memperdalam pembelajaran Anda. Anda juga dapat merespon PAT lain agar kita bisa belajar bersama.
 
-> Untuk studi lanjutan, kami menyarankan mengikuti modul dan jalur pembelajaran [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
+> Untuk studi lanjutan, kami sarankan mengikuti modul dan jalur belajar [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) ini.
 
-**Guru**, kami telah [menyertakan beberapa saran](for-teachers.md) tentang cara menggunakan kurikulum ini.
+**Guru**, kami telah [menyediakan beberapa saran](for-teachers.md) tentang cara menggunakan kurikulum ini.
 
 ---
 
-## Panduan video
+## Video panduan
 
-Beberapa pelajaran tersedia dalam bentuk video singkat. Anda dapat menemukan semuanya di dalam pelajaran, atau di [playlist ML for Beginners di channel Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) dengan mengklik gambar di bawah.
+Beberapa pelajaran tersedia dalam bentuk video singkat. Anda dapat menemukannya secara inline di pelajaran, atau di [playlist ML untuk Pemula di saluran YouTube Microsoft Developer](https://aka.ms/ml-beginners-videos) dengan mengklik gambar di bawah ini.
 
-[![Spanduk ML untuk pemula](../../translated_images/id/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/id/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Kenali Tim
+## Temui Tim
 
-[![Video promo](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
 **Gif oleh** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Klik gambar di atas untuk menonton video tentang proyek dan para pembuatnya!
+> 🎥 Klik gambar di atas untuk video tentang proyek dan orang-orang yang membuatnya!
 
 ---
 
 ## Pedagogi
 
-Kami memilih dua prinsip pedagogis saat menyusun kurikulum ini: memastikan bahwa kurikulum bersifat praktis **berbasis proyek** dan bahwa kurikulum mencakup **kuis yang sering**. Selain itu, kurikulum ini memiliki **tema** umum untuk memberikan kohesi.
+Kami memilih dua prinsip pedagogis saat membangun kurikulum ini: memastikan bahwa itu berbasis **proyek langsung** dan mencakup **kuis yang sering**. Selain itu, kurikulum ini memiliki **tema** umum agar lebih terpadu.
 
-Dengan memastikan konten selaras dengan proyek, proses pembelajaran menjadi lebih menarik bagi siswa dan retensi konsep akan meningkat. Selain itu, kuis dengan taruhan rendah sebelum kelas menetapkan niat siswa untuk mempelajari topik, sementara kuis kedua setelah kelas memastikan retensi lebih lanjut. Kurikulum ini dirancang agar fleksibel dan menyenangkan dan dapat diikuti secara keseluruhan atau sebagian. Proyek dimulai kecil dan menjadi semakin kompleks hingga akhir siklus 12-minggu. Kurikulum ini juga menyertakan postscript tentang aplikasi dunia nyata dari ML, yang dapat digunakan sebagai kredit tambahan atau sebagai dasar diskusi.
+Dengan memastikan isi selaras dengan proyek, proses menjadi lebih menarik bagi siswa dan memperkuat retensi konsep. Selain itu, kuis dengan tingkat tekanan rendah sebelum kelas mengarahkan niat siswa untuk mempelajari topik, sementara kuis kedua setelah kelas memastikan retensi lebih lanjut. Kurikulum ini dirancang agar fleksibel dan menyenangkan serta dapat diambil secara lengkap atau sebagian. Proyek dimulai dari yang sederhana dan menjadi semakin kompleks hingga akhir siklus 12 minggu. Kurikulum ini juga mencakup posskrip tentang aplikasi nyata ML, yang dapat digunakan sebagai nilai tambah atau sebagai dasar diskusi.
 
-> Temukan [Kode Etik kami](CODE_OF_CONDUCT.md), [Panduan Kontribusi](CONTRIBUTING.md), [Panduan Terjemahan](TRANSLATIONS.md), dan [Panduan Pemecahan Masalah](TROUBLESHOOTING.md). Kami menyambut masukan konstruktif Anda!
+> Temukan [Kode Etik](CODE_OF_CONDUCT.md), [Kontribusi](CONTRIBUTING.md), [Terjemahan](TRANSLATIONS.md), dan pedoman [Pemecahan Masalah](TROUBLESHOOTING.md) kami. Kami menyambut umpan balik konstruktif Anda!
 
 ## Setiap pelajaran mencakup
 
 - sketchnote opsional
 - video pelengkap opsional
-- panduan video (hanya beberapa pelajaran)
-- [kuis pemanasan pra-ceramah](https://ff-quizzes.netlify.app/en/ml/)
+- video panduan (hanya beberapa pelajaran)
+- [kuis pemanasan pra-kuliah](https://ff-quizzes.netlify.app/en/ml/)
 - pelajaran tertulis
-- untuk pelajaran berbasis proyek, panduan langkah-demi-langkah tentang cara membangun proyek
+- untuk pelajaran berbasis proyek, panduan langkah demi langkah cara membangun proyek
 - pemeriksaan pengetahuan
-- sebuah tantangan
+- tantangan
 - bacaan pelengkap
 - tugas
-- [kuis pasca-ceramah](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Catatan tentang bahasa**: Pelajaran-pelajaran ini terutama ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk menyelesaikan pelajaran R, buka folder `/solution` dan cari pelajaran R. Mereka memiliki ekstensi .rmd yang merepresentasikan **R Markdown** yang dapat didefinisikan secara sederhana sebagai penyisipan `code chunks` (R atau bahasa lain) dan `YAML header` (yang mengarahkan bagaimana memformat keluaran seperti PDF) dalam sebuah `dokumen Markdown`. Dengan demikian, ini berfungsi sebagai kerangka penulisan contoh untuk data science karena memungkinkan Anda menggabungkan kode, keluaran, dan pemikiran Anda dengan menuliskannya dalam Markdown. Selain itu, dokumen R Markdown dapat dirender ke format keluaran seperti PDF, HTML, atau Word.
-
-> **Catatan tentang kuis**: Semua kuis terdapat di [folder Quiz App](../../quiz-app), total 52 kuis masing-masing berisi tiga pertanyaan. Mereka ditautkan dari dalam pelajaran tetapi aplikasi kuis dapat dijalankan secara lokal; ikuti petunjuk di folder `quiz-app` untuk menyajikan secara lokal atau melakukan deploy ke Azure.
-
-| Nomor Pelajaran |                             Topik                              |                   Pengelompokan Pelajaran                   | Tujuan Pembelajaran                                                                                                             |                                                              Pelajaran Tertaut                                                               |                        Penulis                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Pengantar pembelajaran mesin                |      [Introduction](1-Introduction/README.md)       | Pelajari konsep dasar di balik pembelajaran mesin                                                                                |                                             [Pelajaran](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Sejarah pembelajaran mesin                 |      [Introduction](1-Introduction/README.md)       | Pelajari sejarah yang mendasari bidang ini                                                                                         |                                            [Pelajaran](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 Keadilan dan pembelajaran mesin                  |      [Introduction](1-Introduction/README.md)       | Apa isu filosofis penting terkait keadilan yang harus dipertimbangkan siswa saat membangun dan menerapkan model ML? |                                              [Pelajaran](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Teknik untuk pembelajaran mesin                 |      [Introduction](1-Introduction/README.md)       | Teknik apa yang digunakan peneliti ML untuk membangun model ML?                                                                       |                                          [Pelajaran](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Pengantar regresi                   |        [Regression](2-Regression/README.md)         | Mulai menggunakan Python dan Scikit-learn untuk model regresi                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Harga labu Amerika Utara 🎃                |        [Regression](2-Regression/README.md)         | Visualisasikan dan bersihkan data sebagai persiapan untuk ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Harga labu Amerika Utara 🎃                |        [Regression](2-Regression/README.md)         | Bangun model regresi linier dan polinomial                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                Harga labu Amerika Utara 🎃                |        [Regression](2-Regression/README.md)         | Bangun model regresi logistik                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Aplikasi Web 🔌                          |           [Web App](3-Web-App/README.md)            | Bangun aplikasi web untuk menggunakan model yang telah Anda latih                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Pengantar klasifikasi                 |    [Classification](4-Classification/README.md)     | Bersihkan, persiapkan, dan visualisasikan data Anda; pengantar klasifikasi                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             Masakan Asia dan India yang Lezat 🍜             |    [Classification](4-Classification/README.md)     | Pengantar klasifikator                                                                                                     | [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       |             Masakan Asia dan India yang Lezat 🍜             |    [Classification](4-Classification/README.md)     | Klasifikator lainnya                                                                                                                | [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       |             Masakan Asia dan India yang Lezat 🍜             |    [Classification](4-Classification/README.md)     | Bangun aplikasi web rekomendasi menggunakan model Anda                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Pengantar klastering                   |        [Clustering](5-Clustering/README.md)         | Bersihkan, persiapkan, dan visualisasikan data Anda; pengantar klastering                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Menjelajahi Selera Musik Nigeria 🎧              |        [Clustering](5-Clustering/README.md)         | Jelajahi metode klastering K-Means                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Pengantar pemrosesan bahasa alami ☕️         |   [Natural language processing](6-NLP/README.md)    | Pelajari dasar-dasar NLP dengan membangun bot sederhana                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Tugas NLP Umum ☕️                      |   [Natural language processing](6-NLP/README.md)    | Perdalam pengetahuan NLP Anda dengan memahami tugas umum yang diperlukan saat menangani struktur bahasa                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Terjemahan dan analisis sentimen ♥️              |   [Natural language processing](6-NLP/README.md)    | Terjemahan dan analisis sentimen dengan Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Hotel romantis di Eropa ♥️                  |   [Natural language processing](6-NLP/README.md)    | Analisis sentimen dengan ulasan hotel 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Hotel romantis di Eropa ♥️                  |   [Natural language processing](6-NLP/README.md)    | Analisis sentimen dengan ulasan hotel 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Pengantar peramalan deret waktu             |        [Time series](7-TimeSeries/README.md)        | Pengantar peramalan deret waktu                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Penggunaan Daya Dunia ⚡️ - peramalan deret waktu dengan ARIMA |        [Time series](7-TimeSeries/README.md)        | Peramalan deret waktu dengan ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Penggunaan Daya Dunia ⚡️ - peramalan deret waktu dengan SVR  |        [Time series](7-TimeSeries/README.md)        | Peramalan deret waktu dengan Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Pengantar pembelajaran penguatan             | [Reinforcement learning](8-Reinforcement/README.md) | Pengantar pembelajaran penguatan dengan Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Bantu Peter menghindari serigala! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Catatan   |            Skenario dan aplikasi ML di dunia nyata            |      [ML in the Wild](9-Real-World/README.md)       | Aplikasi dunia nyata yang menarik dan mengungkap dari ML klasik                                                               |                                             [Pelajaran](9-Real-World/1-Applications/README.md)                                              |                         Tim                         |
-|  Catatan   |            Debugging Model dalam ML menggunakan dasbor RAI          |      [ML in the Wild](9-Real-World/README.md)       | Debugging model dalam Machine Learning menggunakan komponen dasbor Responsible AI                                                              |                                             [Pelajaran](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [temukan semua sumber tambahan untuk kursus ini di koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- [kuis pasca-kuliah](https://ff-quizzes.netlify.app/en/ml/)
+
+> **Catatan tentang bahasa**: Pelajaran ini terutama ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk menyelesaikan pelajaran R, buka folder `/solution` dan cari pelajaran R. Pelajaran tersebut memiliki ekstensi .rmd yang merupakan file **R Markdown** yang dapat didefinisikan sebagai gabungan `potongan kode` (dari R atau bahasa lain) dan `header YAML` (yang mengarahkan bagaimana memformat keluaran seperti PDF) dalam dokumen `Markdown`. Dengan demikian, ia berfungsi sebagai kerangka kerja pembuatan yang sangat baik untuk ilmu data karena memungkinkan Anda menggabungkan kode, outputnya, dan pemikiran Anda dengan menuliskannya dalam Markdown. Selain itu, dokumen R Markdown dapat dirender ke format keluaran seperti PDF, HTML, atau Word.
+> **Catatan tentang kuis**: Semua kuis terdapat di dalam [folder Quiz App](../../quiz-app), dengan total 52 kuis yang masing-masing berisi tiga pertanyaan. Kuis tersebut ditautkan di dalam pelajaran, tetapi aplikasi kuis dapat dijalankan secara lokal; ikuti instruksi di folder `quiz-app` untuk menghosting secara lokal atau men-deploy ke Azure.
+
+| Nomor Pelajaran |                             Topik                              |                   Pengelompokan Pelajaran                   | Tujuan Pembelajaran                                                                                                             |                                                              Pelajaran Tautan                                                               |                        Penulis                        |
+| :--------------: | :------------------------------------------------------------: | :----------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|       01         |                Pengenalan machine learning                    |      [Pengenalan](1-Introduction/README.md)                 | Pelajari konsep dasar di balik machine learning                                                                                |                                             [Pelajaran](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|       02         |                Sejarah machine learning                       |      [Pengenalan](1-Introduction/README.md)                 | Pelajari sejarah yang mendasari bidang ini                                                                                     |                                            [Pelajaran](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen dan Amy                      |
+|       03         |                 Keadilan dan machine learning                 |      [Pengenalan](1-Introduction/README.md)                 | Apa isu filosofis penting tentang keadilan yang harus dipertimbangkan oleh siswa saat membangun dan menerapkan model ML?       |                                              [Pelajaran](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|       04         |                Teknik untuk machine learning                  |      [Pengenalan](1-Introduction/README.md)                 | Teknik apa yang digunakan peneliti ML untuk membangun model ML?                                                                 |                                          [Pelajaran](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris dan Jen                     |
+|       05         |                   Pengenalan regresi                          |        [Regresi](2-Regression/README.md)                    | Mulai dengan Python dan Scikit-learn untuk model regresi                                                                        |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|       06         |                Harga labu Amerika Utara 🎃                    |        [Regresi](2-Regression/README.md)                    | Visualisasikan dan bersihkan data sebagai persiapan untuk ML                                                                     |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|       07         |                Harga labu Amerika Utara 🎃                    |        [Regresi](2-Regression/README.md)                    | Bangun model regresi linier dan polinomial                                                                                      |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen dan Dmitry • Eric Wanjau       |
+|       08         |                Harga labu Amerika Utara 🎃                    |        [Regresi](2-Regression/README.md)                    | Bangun model regresi logistik                                                                                                   |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|       09         |                          Aplikasi Web 🔌                       |           [Aplikasi Web](3-Web-App/README.md)                | Bangun aplikasi web untuk menggunakan model yang sudah dilatih                                                                   |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|       10         |                 Pengenalan klasifikasi                        |    [Klasifikasi](4-Classification/README.md)               | Bersihkan, persiapkan, dan visualisasikan data Anda; pengantar klasifikasi                                                      | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen dan Cassie • Eric Wanjau |
+|       11         |             Masakan Asia dan India yang Lezat 🍜              |    [Klasifikasi](4-Classification/README.md)               | Pengenalan kepada pengklasifikasi                                                                                                | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen dan Cassie • Eric Wanjau |
+|       12         |             Masakan Asia dan India yang Lezat 🍜              |    [Klasifikasi](4-Classification/README.md)               | Lebih banyak pengklasifikasi                                                                                                    | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen dan Cassie • Eric Wanjau |
+|       13         |             Masakan Asia dan India yang Lezat 🍜              |    [Klasifikasi](4-Classification/README.md)               | Bangun aplikasi rekomender web menggunakan model Anda                                                                           |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|       14         |                   Pengenalan klastering                       |        [Klastering](5-Clustering/README.md)                 | Bersihkan, persiapkan, dan visualisasikan data Anda; pengantar klastering                                                      |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|       15         |              Menjelajahi Selera Musik Nigeria 🎧              |        [Klastering](5-Clustering/README.md)                 | Jelajahi metode klastering K-Means                                                                                              |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|       16         |        Pengenalan pemrosesan bahasa alami ☕️                  |   [Pemrosesan bahasa alami](6-NLP/README.md)                 | Pelajari dasar-dasar NLP dengan membangun bot sederhana                                                                         |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|       17         |                      Tugas Umum NLP ☕️                        |   [Pemrosesan bahasa alami](6-NLP/README.md)                 | Perdalam pengetahuan NLP Anda dengan memahami tugas umum yang diperlukan saat menangani struktur bahasa                         |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|       18         |             Terjemahan dan analisis sentimen ♥️               |   [Pemrosesan bahasa alami](6-NLP/README.md)                 | Terjemahan dan analisis sentimen dengan Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|       19         |                  Hotel romantis di Eropa ♥️                    |   [Pemrosesan bahasa alami](6-NLP/README.md)                 | Analisis sentimen dengan ulasan hotel 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|       20         |                  Hotel romantis di Eropa ♥️                    |   [Pemrosesan bahasa alami](6-NLP/README.md)                 | Analisis sentimen dengan ulasan hotel 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|       21         |            Pengenalan peramalan deret waktu                   |        [Deret waktu](7-TimeSeries/README.md)                | Pengenalan kepada peramalan deret waktu                                                                                        |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|       22         | ⚡️ Penggunaan Listrik Dunia ⚡️ - peramalan deret waktu dengan ARIMA |        [Deret waktu](7-TimeSeries/README.md)                | Peramalan deret waktu dengan ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|       23         |  ⚡️ Penggunaan Listrik Dunia ⚡️ - peramalan deret waktu dengan SVR  |        [Deret waktu](7-TimeSeries/README.md)                | Peramalan deret waktu dengan Support Vector Regressor                                                                          |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|       24         |             Pengenalan pembelajaran penguatan                  | [Pembelajaran penguatan](8-Reinforcement/README.md)         | Pengenalan pembelajaran penguatan dengan Q-Learning                                                                            |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|       25         |                 Bantu Peter menghindari serigala! 🐺          | [Pembelajaran penguatan](8-Reinforcement/README.md)         | Pembelajaran penguatan Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  Postscript     |            Skenario dan aplikasi ML di dunia nyata             |      [ML di Dunia Nyata](9-Real-World/README.md)            | Aplikasi ML klasik yang menarik dan mengungkap dalam dunia nyata                                                                |                                             [Pelajaran](9-Real-World/1-Applications/README.md)                                              |                         Tim                          |
+|  Postscript     |            Debugging Model dalam ML menggunakan dasbor RAI     |      [ML di Dunia Nyata](9-Real-World/README.md)            | Debugging Model dalam Machine Learning menggunakan komponen dasbor Responsible AI                                               |                                             [Pelajaran](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+
+> [temukan semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## Akses offline
 
-Anda dapat menjalankan dokumentasi ini secara offline dengan menggunakan [Docsify](https://docsify.js.org/#/). Fork repo ini, [pasang Docsify](https://docsify.js.org/#/quickstart) di mesin lokal Anda, dan kemudian di folder root repo ini, ketik `docsify serve`. Situs web akan disajikan pada port 3000 di localhost Anda: `localhost:3000`.
-
-## Berkas PDF
+Anda dapat menjalankan dokumentasi ini secara offline dengan menggunakan [Docsify](https://docsify.js.org/#/). Fork repo ini, [instal Docsify](https://docsify.js.org/#/quickstart) pada mesin lokal Anda, kemudian di folder root repo ini, ketik `docsify serve`. Situs web akan disajikan pada port 3000 di localhost Anda: `localhost:3000`.
 
-Temukan file pdf kurikulum dengan tautan [di sini](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+## PDF
 
+Temukan pdf kurikulum dengan tautan [di sini](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
-## 🎒 Kursus Lainnya 
+## 🎒 Kursus Lainnya
 
-Tim kami membuat kursus lain! Lihat:
+Tim kami memproduksi kursus lainnya! Cek:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -179,45 +187,45 @@ Tim kami membuat kursus lain! Lihat:
 [![Agen AI untuk Pemula](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)
 
 ---
- 
+
 ### Seri AI Generatif
-[![AI Generatif untuk Pemula](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![AI Generatif (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)
-[![AI Generatif (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Pembelajaran Inti
-[![ML untuk Pemula](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 untuk Pemula](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 untuk Pemula](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)
-[![Keamanan Siber untuk Pemula](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)
-[![Pengembangan Web untuk Pemula](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 untuk Pemula](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)
-[![Pengembangan XR untuk Pemula](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Seri Copilot
-[![Copilot untuk Pemrograman Berpasangan AI](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 untuk 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)
-[![Petualangan Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
 ## Mendapatkan Bantuan
 
-Jika Anda mengalami kebingungan atau memiliki pertanyaan tentang membangun aplikasi AI, bergabunglah dengan sesama pelajar dan pengembang berpengalaman dalam diskusi tentang MCP. Ini adalah komunitas yang mendukung di mana pertanyaan disambut dan pengetahuan dibagikan secara bebas.
+Jika Anda mengalami kebuntuan atau memiliki pertanyaan tentang membangun aplikasi AI. Bergabunglah dengan sesama pelajar dan pengembang berpengalaman dalam diskusi tentang MCP. Ini adalah komunitas yang suportif di mana pertanyaan disambut dan pengetahuan dibagikan dengan bebas.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Jika Anda memiliki masukan produk atau menemukan kesalahan saat membangun, kunjungi:
+Jika Anda memiliki umpan balik produk atau menemui kesalahan saat membangun, kunjungi:
 
-[![Forum Pengembang Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**Penafian**:
-Dokumen ini telah diterjemahkan menggunakan layanan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha mencapai ketepatan, harap diketahui bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidaktepatan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat krusial, disarankan menggunakan jasa penerjemah profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau salah tafsir yang timbul dari penggunaan terjemahan ini.
+**Penafian**:  
+Dokumen ini telah diterjemahkan menggunakan layanan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk akurasi, harap diketahui bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang sahih. Untuk informasi penting, disarankan menggunakan terjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang salah yang timbul dari penggunaan terjemahan ini.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/ms/README.md b/translations/ms/README.md
index b74cbb6b9..634f162a8 100644
--- a/translations/ms/README.md
+++ b/translations/ms/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T12:43:55+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T14:10:33+00:00",
   "source_file": "README.md",
   "language_code": "ms"
 }
@@ -10,160 +10,169 @@ CO_OP_TRANSLATOR_METADATA:
 [![Lesen GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
 [![Penyumbang GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
 [![Isu GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![Pull Request GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs Dipersilakan](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![Permintaan tarik GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![Selamat datang PR](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![Pemerhati GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![Fork GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![Penonton GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![Garpu GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
 [![Bintang GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
-### 🌐 Sokongan Berbilang Bahasa
+### 🌐 Sokongan Pelbagai Bahasa
 
-#### Disokong melalui GitHub Action (Automatik & Sentiasa Terkini)
+#### Disokong melalui Tindakan GitHub (Automatik & Sentiasa Dikemas Kini)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arab](../ar/README.md) | [Bengali](../bn/README.md) | [Bahasa Bulgaria](../bg/README.md) | [Bahasa Burma (Myanmar)](../my/README.md) | [Cina (Dipermudahkan)](../zh/README.md) | [Cina (Tradisional, Hong Kong)](../hk/README.md) | [Cina (Tradisional, Macau)](../mo/README.md) | [Cina (Tradisional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Bahasa Denmark](../da/README.md) | [Belanda](../nl/README.md) | [Bahasa Estonia](../et/README.md) | [Bahasa Finland](../fi/README.md) | [Perancis](../fr/README.md) | [Jerman](../de/README.md) | [Yunani](../el/README.md) | [Ibrani](../he/README.md) | [Hindi](../hi/README.md) | [Hungari](../hu/README.md) | [Indonesia](../id/README.md) | [Itali](../it/README.md) | [Jepun](../ja/README.md) | [Kannada](../kn/README.md) | [Korea](../ko/README.md) | [Bahasa Lithuania](../lt/README.md) | [Melayu](./README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeria](../pcm/README.md) | [Bahasa Norway](../no/README.md) | [Parsi (Farsi)](../fa/README.md) | [Poland](../pl/README.md) | [Portugis (Brazil)](../br/README.md) | [Portugis (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romania](../ro/README.md) | [Rusia](../ru/README.md) | [Serbia (Cyrillic)](../sr/README.md) | [Slovakia](../sk/README.md) | [Slovenia](../sl/README.md) | [Sepanyol](../es/README.md) | [Swahili](../sw/README.md) | [Bahasa Sweden](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turki](../tr/README.md) | [Ukraine](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnam](../vi/README.md)
+[Arab](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgaria](../bg/README.md) | [Burma (Myanmar)](../my/README.md) | [Cina (Ringkas)](../zh/README.md) | [Cina (Tradisional, Hong Kong)](../hk/README.md) | [Cina (Tradisional, Macau)](../mo/README.md) | [Cina (Tradisional, Taiwan)](../tw/README.md) | [Croatia](../hr/README.md) | [Szech](../cs/README.md) | [Denmark](../da/README.md) | [Belanda](../nl/README.md) | [Estonia](../et/README.md) | [Finland](../fi/README.md) | [Perancis](../fr/README.md) | [Jerman](../de/README.md) | [Yunani](../el/README.md) | [Ibrani](../he/README.md) | [Hindi](../hi/README.md) | [Hungary](../hu/README.md) | [Indonesia](../id/README.md) | [Itali](../it/README.md) | [Jepun](../ja/README.md) | [Kannada](../kn/README.md) | [Korea](../ko/README.md) | [Lithuania](../lt/README.md) | [Melayu](./README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Pidgin Nigeria](../pcm/README.md) | [Norway](../no/README.md) | [Parsi (Farsi)](../fa/README.md) | [Poland](../pl/README.md) | [Portugis (Brazil)](../br/README.md) | [Portugis (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romania](../ro/README.md) | [Rusia](../ru/README.md) | [Serbia (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenia](../sl/README.md) | [Sepanyol](../es/README.md) | [Swahili](../sw/README.md) | [Sweden](../sv/README.md) | [Tagalog (Filipina)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turki](../tr/README.md) | [Ukraine](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnam](../vi/README.md)
+
+> **Lebih suka Klon Secara Tempatan?**
+
+> Repositori ini termasuk 50+ terjemahan bahasa yang secara signifikan meningkatkan saiz muat turun. Untuk klon tanpa terjemahan, gunakan 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'
+> ```
+> Ini memberi anda segala yang anda perlukan untuk menyelesaikan kursus dengan muat turun yang jauh lebih cepat.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### Sertai Komuniti Kami
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Kami mempunyai siri Learn with AI di Discord yang sedang berjalan, ketahui lebih lanjut dan sertai kami di [Siri Belajar dengan AI](https://aka.ms/learnwithai/discord) dari 18 - 30 September, 2025. Anda akan mendapat petua dan trik menggunakan GitHub Copilot untuk Sains Data.
+Kami sedang menjalankan siri Discord belajar dengan AI, ketahui lebih lanjut dan sertai kami di [Siri Belajar dengan AI](https://aka.ms/learnwithai/discord) dari 18 - 30 September, 2025. Anda akan mendapat petua dan trik menggunakan GitHub Copilot untuk Sains Data.
 
-![Siri Belajar dengan AI](../../translated_images/ms/3.9b58fd8d6c373c20.webp)
+![Siri belajar dengan AI](../../../../translated_images/ms/3.9b58fd8d6c373c20.webp)
 
-# Machine Learning for Beginners - A Curriculum
+# Pembelajaran Mesin untuk Pemula - Kurikulum
 
-> 🌍 Jelajah ke seluruh dunia semasa kita meneroka Pembelajaran Mesin melalui budaya dunia 🌍
+> 🌍 Melancong ke seluruh dunia sambil meneroka Pembelajaran Mesin melalui budaya dunia 🌍
 
-Cloud Advocates di Microsoft gembira untuk menawarkan kurikulum 12 minggu, 26 pelajaran yang semuanya mengenai **Machine Learning**. Dalam kurikulum ini, anda akan belajar tentang apa yang kadang-kadang dipanggil **pembelajaran mesin klasik**, menggunakan terutamanya Scikit-learn sebagai perpustakaan dan mengelakkan pembelajaran mendalam, yang diliputi dalam [kurikulum 'AI untuk Pemula'](https://aka.ms/ai4beginners). Padankan pelajaran ini dengan [kurikulum 'Data Science for Beginners'](https://aka.ms/ds4beginners), juga!
+Penggerak Awan di Microsoft dengan sukacitanya menawarkan kurikulum 12 minggu, 26 pelajaran mengenai **Pembelajaran Mesin**. Dalam kurikulum ini, anda akan mempelajari apa yang kadang-kala dipanggil **pembelajaran mesin klasik**, menggunakan terutamanya Scikit-learn sebagai perpustakaan dan mengelakkan pembelajaran mendalam, yang dibincangkan dalam [kurikulum AI untuk Pemula](https://aka.ms/ai4beginners) kami. Gabungkan pelajaran ini dengan ['Kurikulum Sains Data untuk Pemula'](https://aka.ms/ds4beginners) kami juga!
 
-Jelajah dengan kami ke seluruh dunia semasa kami menerapkan teknik klasik ini kepada data dari banyak kawasan dunia. Setiap pelajaran termasuk kuiz pra- dan pasca-pelajaran, arahan bertulis untuk menyelesaikan pelajaran, satu penyelesaian, satu tugasan, dan banyak lagi. Pedagogi berasaskan projek membolehkan anda belajar sambil membina, satu cara yang terbukti untuk kemahiran baru 'melekat'.
+Bersama kami mengembara ke seluruh dunia sambil menerapkan teknik klasik ini pada data dari banyak kawasan di dunia. Setiap pelajaran merangkumi kuiz sebelum dan selepas pelajaran, arahan bertulis untuk menyelesaikan pelajaran, penyelesaian, tugasan, dan banyak lagi. Pendekatan berasaskan projek kami membolehkan anda belajar sambil membina, cara terbukti untuk kemahiran baru 'melekat'.
 
-**✍️ Terima kasih tidak terhingga kepada penulis kami** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu dan Amy Boyd
+**✍️ Terima kasih yang tidak terhingga kepada penulis kami** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu dan Amy Boyd
 
-**🎨 Terima kasih juga kepada ilustrator kami** Tomomi Imura, Dasani Madipalli, dan Jen Looper
+**🎨 Terima kasih juga kepada pelukis ilustrasi kami** Tomomi Imura, Dasani Madipalli, dan Jen Looper
 
-**🙏 Terima kasih khas 🙏 kepada penulis, penilai, dan penyumbang kandungan Microsoft Student Ambassador kami**, terutamanya Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, dan Snigdha Agarwal
+**🙏 Terima kasih khas 🙏 kepada penulis, penyemak dan penyumbang kandungan Duta Pelajar Microsoft kami**, terutamanya Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, dan Snigdha Agarwal
 
-**🤩 Penghargaan tambahan kepada Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, dan Vidushi Gupta untuk pelajaran R kami!**
+**🤩 Penghargaan tambahan kepada Duta Pelajar Microsoft Eric Wanjau, Jasleen Sondhi, dan Vidushi Gupta untuk pelajaran R kami!**
 
-# Memulakan
+# Mulakan
 
 Ikuti langkah-langkah ini:
-1. **Fork Repositori**: Klik butang "Fork" di penjuru atas-kanan halaman ini.
-2. **Clone Repositori**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **Fork Repositori**: Klik butang "Fork" di sudut kanan atas halaman ini.
+2. **Klon Repositori**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [temui semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [cari semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Perlu bantuan?** Semak [Panduan Penyelesaian Masalah](TROUBLESHOOTING.md) kami untuk penyelesaian kepada isu biasa berkaitan pemasangan, tetapan, dan menjalankan pelajaran.
+> 🔧 **Perlukan bantuan?** Semak [Panduan Penyelesaian Masalah](TROUBLESHOOTING.md) kami untuk penyelesaian masalah biasa berkaitan pemasangan, persediaan, dan menjalankan pelajaran.
 
 
-**[Pelajar](https://aka.ms/student-page)**, untuk menggunakan kurikulum ini, fork keseluruhan repo ke akaun GitHub anda sendiri dan selesaikan latihan secara sendiri atau bersama kumpulan:
+**[Pelajar](https://aka.ms/student-page)**, untuk menggunakan kurikulum ini, fork seluruh repositori ke akaun GitHub anda sendiri dan selesaikan latihan sendiri atau bersama kumpulan:
 
 - Mulakan dengan kuiz pra-ceramah.
-- Baca ceramah dan lengkapkan aktiviti, berhenti dan renungkan setiap pemeriksaan pengetahuan.
-- Cuba buat projek dengan memahami pelajaran daripada menjalankan kod penyelesaian; bagaimanapun kod tersebut tersedia dalam folder `/solution` dalam setiap pelajaran berorientasikan projek.
-- Ambil kuiz pasca-ceramah.
-- Lengkapkan cabaran.
-- Lengkapkan tugasan.
-- Selepas menyelesaikan kumpulan pelajaran, lawati [Papan Perbincangan](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar secara terbuka" dengan mengisi rubrik PAT yang sesuai. 'PAT' ialah Progress Assessment Tool (Alat Penilaian Kemajuan) iaitu rubrik yang anda isi untuk meningkatkan pembelajaran anda. Anda juga boleh memberi reaksi kepada PAT lain supaya kita boleh belajar bersama.
+- Baca kuliah dan lengkapkan aktiviti, berhenti dan renungkan setiap pemeriksaan pengetahuan.
+- Cuba cipta projek dengan memahami pelajaran daripada menjalankan kod penyelesaian; walau bagaimanapun kod itu tersedia dalam folder `/solution` dalam setiap pelajaran berorientasikan projek.
+- Ambil kuiz selepas kuliah.
+- Selesaikan cabaran.
+- Selesaikan tugasan.
+- Selepas menamatkan satu kumpulan pelajaran, lawati [Papan Perbincangan](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar dengan lantang" dengan mengisi rubrik PAT yang sesuai. 'PAT' adalah Alat Penilaian Kemajuan yang merupakan rubrik anda mengisinya untuk meningkatkan pembelajaran. Anda juga boleh memberi reaksi kepada PAT lain supaya kita boleh belajar bersama.
 
-> Untuk kajian lanjut, kami mengesyorkan mengikuti modul dan laluan pembelajaran [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
+> Untuk pengajian lanjut, kami mengesyorkan mengikuti modul dan laluan pembelajaran [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) ini.
 
-**Guru**, kami telah [menyertakan beberapa cadangan](for-teachers.md) mengenai cara menggunakan kurikulum ini.
+**Guru**, kami telah [menyediakan beberapa cadangan](for-teachers.md) tentang cara menggunakan kurikulum ini.
 
 ---
 
-## Panduan video
+## Panduan Video
 
-Beberapa pelajaran tersedia sebagai video bentuk pendek. Anda boleh mencari semua ini bersusun dalam pelajaran, atau di [playlist ML for Beginners pada saluran Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) dengan mengklik imej di bawah.
+Sesetengah pelajaran tersedia dalam bentuk video pendek. Anda boleh menemui kesemua ini secara sebaris dalam pelajaran, atau di [senarai main ML untuk Pemula di saluran YouTube Microsoft Developer](https://aka.ms/ml-beginners-videos) dengan mengklik imej di bawah.
 
-[![Sepanduk ML untuk Pemula](../../translated_images/ms/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![Sepanduk ML untuk pemula](../../../../translated_images/ms/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Temui Pasukan
+## Kenali Pasukan
 
 [![Video promosi](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**Gif oleh** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**GIF oleh** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Klik imej di atas untuk menonton video tentang projek dan orang-orang yang menciptakannya!
+> 🎥 Klik imej di atas untuk video tentang projek dan orang yang menciptanya!
 
 ---
 
 ## Pedagogi
 
-Kami telah memilih dua prinsip pedagogi semasa membina kurikulum ini: memastikan ia bersifat praktikal **berasaskan projek** dan bahawa ia merangkumi **kuiz yang kerap**. Selain itu, kurikulum ini mempunyai **tema** yang sama untuk memberikan kesinambungan.
+Kami memilih dua prinsip pedagogi ketika membangunkan kurikulum ini: memastikan ia berasaskan projek yang praktikal dan termasuk kuiz yang kerap. Selain itu, kurikulum ini mempunyai tema umum untuk memberikan keseragaman.
 
-Dengan memastikan kandungan selari dengan projek, proses menjadi lebih menarik untuk pelajar dan pengekalan konsep akan dipertingkatkan. Selain itu, kuiz berisiko rendah sebelum kelas menetapkan niat pelajar untuk mempelajari topik, manakala kuiz kedua selepas kelas memastikan pengekalan lanjut. Kurikulum ini direka untuk fleksibel dan menyeronokkan dan boleh diambil secara keseluruhan atau sebahagian. Projek bermula kecil dan menjadi semakin kompleks menjelang akhir kitaran 12 minggu. Kurikulum ini juga termasuk posskrip mengenai aplikasi dunia sebenar ML, yang boleh digunakan sebagai kredit tambahan atau sebagai asas untuk perbincangan.
+Dengan memastikan kandungan selari dengan projek, proses pembelajaran menjadi lebih menarik bagi pelajar dan pengekalan konsep akan bertambah baik. Selain itu, kuiz rendah risiko sebelum kelas menetapkan niat pelajar untuk mempelajari topik, sementara kuiz kedua selepas kelas memastikan pengekalan yang lebih baik. Kurikulum ini direka supaya fleksibel dan menyeronokkan dan boleh diambil secara keseluruhan atau sebahagian. Projek bermula kecil dan menjadi semakin kompleks pada penghujung kitaran 12 minggu. Kurikulum ini juga termasuk posskrip mengenai aplikasi dunia sebenar ML, yang boleh digunakan sebagai kredit tambahan atau sebagai asas perbincangan.
 
-> Temui [Kod Tingkah Laku](CODE_OF_CONDUCT.md), [Sumbangan](CONTRIBUTING.md), [Terjemahan](TRANSLATIONS.md), dan garis panduan [Penyelesaian Masalah](TROUBLESHOOTING.md) kami. Kami mengalu-alukan maklum balas membina anda!
+> Dapatkan [Kod Etika](CODE_OF_CONDUCT.md), [Penyumbangan](CONTRIBUTING.md), [Terjemahan](TRANSLATIONS.md), dan panduan [Penyelesaian Masalah](TROUBLESHOOTING.md) kami. Kami mengalu-alukan maklum balas membina anda!
 
-## Setiap pelajaran termasuk
+## Setiap pelajaran merangkumi
 
 - nota lakaran pilihan
 - video tambahan pilihan
 - panduan video (sesetengah pelajaran sahaja)
 - [kuiz pemanasan pra-ceramah](https://ff-quizzes.netlify.app/en/ml/)
 - pelajaran bertulis
-- untuk pelajaran berasaskan projek, panduan langkah demi langkah tentang cara membina projek
+- untuk pelajaran berasaskan projek, panduan langkah demi langkah untuk membina projek
 - pemeriksaan pengetahuan
-- sebuah cabaran
-- bahan bacaan tambahan
+- cabaran
+- bacaan tambahan
 - tugasan
 - [kuiz pasca-ceramah](https://ff-quizzes.netlify.app/en/ml/)
 
-> **Catatan tentang bahasa**: Pelajaran ini terutamanya ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk melengkapkan pelajaran R, pergi ke folder `/solution` dan cari pelajaran R. Mereka termasuk sambungan .rmd yang mewakili fail **R Markdown** yang boleh didefinisikan secara ringkas sebagai penempatan `code chunks` (R atau bahasa lain) dan `YAML header` (yang membimbing cara memformat output seperti PDF) dalam satu `Markdown document`. Oleh itu, ia berfungsi sebagai rangka kerja penulisan contoh untuk sains data kerana ia membolehkan anda menggabungkan kod anda, outputnya, dan pemikiran anda dengan membenarkan anda menulisnya dalam Markdown. Selain itu, dokumen R Markdown boleh dirender kepada format output seperti PDF, HTML, atau Word.
-
-> **Catatan tentang kuiz**: Semua kuiz terkandung dalam [folder Quiz App](../../quiz-app), untuk 52 kuiz keseluruhan dengan tiga soalan setiap satu. Mereka dipautkan dari dalam pelajaran tetapi aplikasi kuiz boleh dijalankan secara tempatan; ikut arahan dalam folder `quiz-app` untuk menghoskan secara tempatan atau mendeploy ke Azure.
-
-| Nombor Pelajaran |                             Topik                              |                   Pengelompokan Pelajaran                   | Objektif Pembelajaran                                                                                                             |                                                              Pelajaran Dipautkan                                                               |                        Pengarang                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Pengenalan kepada pembelajaran mesin                |      [Pengenalan](1-Introduction/README.md)       | Pelajari konsep asas di sebalik pembelajaran mesin                                                                                |                                             [Pelajaran](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Sejarah pembelajaran mesin                 |      [Pengenalan](1-Introduction/README.md)       | Pelajari sejarah yang mendasari bidang ini                                                                                         |                                            [Pelajaran](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen dan Amy                      |
-|      03       |                 Keadilan dan pembelajaran mesin                  |      [Pengenalan](1-Introduction/README.md)       | Apakah isu falsafah penting mengenai keadilan yang patut dipertimbangkan pelajar apabila membina dan menerapkan model ML? |                                              [Pelajaran](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Teknik untuk pembelajaran mesin                 |      [Pengenalan](1-Introduction/README.md)       | Apakah teknik yang digunakan penyelidik ML untuk membina model ML?                                                                       |                                          [Pelajaran](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris dan Jen                     |
-|      05       |                   Pengenalan kepada regresi                   |        [Regresi](2-Regression/README.md)         | Mula dengan Python dan Scikit-learn untuk model regresi                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Harga labu Amerika Utara 🎃                |        [Regresi](2-Regression/README.md)         | Visualisasikan dan bersihkan data sebagai persediaan untuk ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Harga labu Amerika Utara 🎃                |        [Regresi](2-Regression/README.md)         | Bina model regresi linear dan polinomial                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen dan Dmitry • Eric Wanjau       |
-|      08       |                Harga labu Amerika Utara 🎃                |        [Regresi](2-Regression/README.md)         | Bina model regresi logistik                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Aplikasi Web 🔌                          |           [Aplikasi Web](3-Web-App/README.md)            | Bina aplikasi web untuk menggunakan model yang telah dilatih                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Pengenalan kepada klasifikasi                 |    [Klasifikasi](4-Classification/README.md)     | Bersihkan, sediakan, dan visualisasikan data anda; pengenalan kepada klasifikasi                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen dan Cassie • Eric Wanjau |
-|      11       |             Masakan Asia dan India yang Lazat 🍜             |    [Klasifikasi](4-Classification/README.md)     | Pengenalan kepada pengklasifikasi                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen dan Cassie • Eric Wanjau |
-|      12       |             Masakan Asia dan India yang Lazat 🍜             |    [Klasifikasi](4-Classification/README.md)     | Lebih banyak pengklasifikasi                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen dan Cassie • Eric Wanjau |
-|      13       |             Masakan Asia dan India yang Lazat 🍜             |    [Klasifikasi](4-Classification/README.md)     | Bina aplikasi web cadangan menggunakan model anda                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Pengenalan kepada pengelompokan                   |        [Pengelompokan](5-Clustering/README.md)         | Bersihkan, sediakan, dan visualisasikan data anda; pengenalan kepada pengelompokan                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Meneroka Selera Muzik Nigeria 🎧              |        [Pengelompokan](5-Clustering/README.md)         | Terokai kaedah pengelompokan K-Means                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Pengenalan kepada pemprosesan bahasa semula jadi ☕️         |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)    | Pelajari asas NLP dengan membina bot ringkas                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Tugas NLP Biasa ☕️                      |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)    | Perdalam pengetahuan NLP anda dengan memahami tugas biasa yang diperlukan apabila berurusan dengan struktur bahasa                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Terjemahan dan analisis sentimen ♥️              |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)    | Terjemahan dan analisis sentimen dengan Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Hotel romantik di Eropah ♥️                  |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)    | Analisis sentimen dengan ulasan hotel 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Hotel romantik di Eropah ♥️                  |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)    | Analisis sentimen dengan ulasan hotel 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Pengenalan kepada ramalan siri masa             |        [Siri masa](7-TimeSeries/README.md)        | Pengenalan kepada ramalan siri masa                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Penggunaan Tenaga Dunia ⚡️ - ramalan siri masa dengan ARIMA |        [Siri masa](7-TimeSeries/README.md)        | Ramalan siri masa dengan ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Penggunaan Tenaga Dunia ⚡️ - ramalan siri masa dengan SVR  |        [Siri masa](7-TimeSeries/README.md)        | Ramalan siri masa dengan Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Pengenalan kepada pembelajaran penguatan             | [Pembelajaran penguatan](8-Reinforcement/README.md) | Pengenalan kepada pembelajaran penguatan dengan Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Bantu Peter elakkan serigala! 🐺                  | [Pembelajaran penguatan](8-Reinforcement/README.md) | Pembelajaran penguatan Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postskrip   |            Senario dan aplikasi ML Dunia Sebenar            |      [ML in the Wild](9-Real-World/README.md)       | Aplikasi dunia sebenar yang menarik dan mendedahkan bagi ML klasik                                                               |                                             [Pelajaran](9-Real-World/1-Applications/README.md)                                              |                         Pasukan                         |
-|  Postskrip   |            Penyahpepijatan Model dalam ML menggunakan papan pemuka RAI          |      [ML in the Wild](9-Real-World/README.md)       | Penyahpepijatan model dalam Pembelajaran Mesin menggunakan komponen papan pemuka Responsible AI                                                              |                                             [Pelajaran](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+> **Nota tentang bahasa**: Pelajaran ini kebanyakannya ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk melengkapkan pelajaran R, pergi ke folder `/solution` dan cari pelajaran R. Ia termasuk sambungan .rmd yang mewakili fail **R Markdown** yang boleh dijelaskan sebagai gabungan `keping kod` (dalam R atau bahasa lain) dan `header YAML` (yang mengarahkan bagaimana format output seperti PDF) dalam `dokumen Markdown`. Oleh itu, ia menjadi kerangka pengarang contoh untuk sains data kerana ia membolehkan anda menggabungkan kod, outputnya, dan pemikiran anda dengan membenarkan anda menulisnya dalam Markdown. Selain itu, dokumen R Markdown boleh dihasilkan ke format output seperti PDF, HTML, atau Word.
+> **Nota mengenai kuiz**: Semua kuiz terkandung dalam [folder Aplikasi Kuiz](../../quiz-app), dengan jumlah 52 kuiz, setiap satu mengandungi tiga soalan. Ia dihubungkan dari dalam pelajaran tetapi aplikasi kuiz boleh dijalankan secara tempatan; ikut arahan dalam folder `quiz-app` untuk hos tempatan atau jalankan ke Azure.
+
+| Nombor Pelajaran |                             Topik                               |                   Pengelompokan Pelajaran                   | Objektif Pembelajaran                                                                                                         |                                                              Pelajaran Berkaitan                                                               |                        Pengarang                        |
+| :---------------: | :------------------------------------------------------------: | :----------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :-----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------: |
+|        01         |                Pengenalan kepada pembelajaran mesin           |      [Pengenalan](1-Introduction/README.md)                  | Pelajari konsep asas di sebalik pembelajaran mesin                                                                            |                                             [Pelajaran](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                         |
+|        02         |                Sejarah pembelajaran mesin                      |      [Pengenalan](1-Introduction/README.md)                  | Pelajari sejarah di sebalik bidang ini                                                                                        |                                            [Pelajaran](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen dan Amy                        |
+|        03         |                 Keadilan dan pembelajaran mesin                |      [Pengenalan](1-Introduction/README.md)                  | Apa isu falsafah penting tentang keadilan yang harus dipertimbangkan pelajar ketika membina dan menggunakan model ML?         |                                              [Pelajaran](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                          |
+|        04         |                Teknik untuk pembelajaran mesin                 |      [Pengenalan](1-Introduction/README.md)                  | Teknik apa yang digunakan penyelidik ML untuk membina model ML?                                                               |                                          [Pelajaran](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris dan Jen                       |
+|        05         |                   Pengenalan kepada regresi                    |        [Regresi](2-Regression/README.md)                     | Mula menggunakan Python dan Scikit-learn untuk model regresi                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau         |
+|        06         |                Harga labu Amerika Utara 🎃                     |        [Regresi](2-Regression/README.md)                     | Visualisasikan dan bersihkan data sebagai persediaan untuk ML                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau         |
+|        07         |                Harga labu Amerika Utara 🎃                     |        [Regresi](2-Regression/README.md)                     | Bina model regresi linear dan polinomial                                                                                       |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen dan Dmitry • Eric Wanjau         |
+|        08         |                Harga labu Amerika Utara 🎃                     |        [Regresi](2-Regression/README.md)                     | Bina model regresi logistik                                                                                                    |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau         |
+|        09         |                          Aplikasi Web 🔌                        |           [Aplikasi Web](3-Web-App/README.md)                 | Bina aplikasi web untuk menggunakan model yang telah dilatih                                                                   |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                            |
+|        10         |                 Pengenalan kepada klasifikasi                  |    [Klasifikasi](4-Classification/README.md)                 | Bersihkan, sediakan, dan visualisasikan data anda; pengenalan kepada klasifikasi                                               | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen dan Cassie • Eric Wanjau   |
+|        11         |             Masakan Asia dan India yang lazat 🍜               |    [Klasifikasi](4-Classification/README.md)                 | Pengenalan kepada pengelasan                                                                                                   | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen dan Cassie • Eric Wanjau   |
+|        12         |             Masakan Asia dan India yang lazat 🍜               |    [Klasifikasi](4-Classification/README.md)                 | Lebih banyak pengelasan                                                                                                        | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen dan Cassie • Eric Wanjau   |
+|        13         |             Masakan Asia dan India yang lazat 🍜               |    [Klasifikasi](4-Classification/README.md)                 | Bina aplikasi web pencadang menggunakan model anda                                                                             |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                            |
+|        14         |                   Pengenalan kepada pengelompokan              |        [Pengelompokan](5-Clustering/README.md)               | Bersihkan, sediakan, dan visualisasikan data anda; pengenalan kepada pengelompokan                                             |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau         |
+|        15         |              Meneroka citarasa muzik Nigeria 🎧                |        [Pengelompokan](5-Clustering/README.md)               | Terokai kaedah K-Means clustering                                                                                              |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau         |
+|        16         |        Pengenalan kepada pemprosesan bahasa semula jadi ☕️    |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)           | Pelajari asas mengenai NLP dengan membina bot mudah                                                                           |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                          |
+|        17         |                      Tugasan umum NLP ☕️                      |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)           | Mendalami pengetahuan NLP anda dengan memahami tugasan umum yang diperlukan ketika mengendalikan struktur bahasa              |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                          |
+|        18         |             Penterjemahan dan analisis sentimen ♥️              |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)           | Penterjemahan dan analisis sentimen dengan Jane Austen                                                                         |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                          |
+|        19         |                  Hotel romantik di Eropah ♥️                   |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)           | Analisis sentimen dengan ulasan hotel 1                                                                                        |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                          |
+|        20         |                  Hotel romantik di Eropah ♥️                   |   [Pemprosesan bahasa semula jadi](6-NLP/README.md)           | Analisis sentimen dengan ulasan hotel 2                                                                                        |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                          |
+|        21         |            Pengenalan kepada ramalan siri masa                 |        [Siri masa](7-TimeSeries/README.md)                    | Pengenalan kepada ramalan siri masa                                                                                            |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                         |
+|        22         | ⚡️ Penggunaan Kuasa Dunia ⚡️ - ramalan siri masa dengan ARIMA |        [Siri masa](7-TimeSeries/README.md)                    | Ramalan siri masa dengan ARIMA                                                                                                 |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                         |
+|        23         |  ⚡️ Penggunaan Kuasa Dunia ⚡️ - ramalan siri masa dengan SVR  |        [Siri masa](7-TimeSeries/README.md)                    | Ramalan siri masa dengan Support Vector Regressor                                                                             |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                          |
+|        24         |             Pengenalan kepada pembelajaran penguatan          | [Pembelajaran penguatan](8-Reinforcement/README.md)           | Pengenalan kepada pembelajaran penguatan dengan Q-Learning                                                                     |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                          |
+|        25         |                 Bantu Peter mengelak dari serigala! 🐺          | [Pembelajaran penguatan](8-Reinforcement/README.md)           | Pembelajaran penguatan Gym                                                                                                     |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                          |
+|  Catatan Akhir   |            Senario dan aplikasi ML Dunia Sebenar               |      [ML di Alam Liar](9-Real-World/README.md)                | Aplikasi ML klasik dunia sebenar yang menarik dan mendedahkan                                                                  |                                             [Pelajaran](9-Real-World/1-Applications/README.md)                                              |                         Pasukan                        |
+|  Catatan Akhir   |          Penyahpepijatan Model dalam ML menggunakan papan pemuka RAI          |      [ML di Alam Liar](9-Real-World/README.md)                | Penyahpepijatan Model dalam Pembelajaran Mesin menggunakan komponen papan pemuka Responsible AI                                  |                                             [Pelajaran](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                      |
 
 > [cari semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## Akses luar talian
 
-Anda boleh menjalankan dokumentasi ini secara luar talian dengan menggunakan [Docsify](https://docsify.js.org/#/). Fork repo ini, [pasang Docsify](https://docsify.js.org/#/quickstart) pada mesin setempat anda, dan kemudian di folder root repo ini, taip `docsify serve`. Laman web akan dihidangkan pada port 3000 pada localhost anda: `localhost:3000`.
+Anda boleh menjalankan dokumentasi ini secara luar talian dengan menggunakan [Docsify](https://docsify.js.org/#/). Cawangan repo ini, [pasang Docsify](https://docsify.js.org/#/quickstart) pada mesin tempatan anda, dan kemudian di folder akar repo ini, taip `docsify serve`. Laman web akan dihidangkan pada port 3000 di localhost anda: `localhost:3000`.
 
-## PDFs
+## PDF
 
 Cari pdf kurikulum dengan pautan [di sini](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
 ## 🎒 Kursus Lain 
 
-Pasukan kami menghasilkan kursus lain! Lihat:
+Pasukan kami menghasilkan kursus lain! Semak:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -172,26 +181,26 @@ Pasukan kami menghasilkan kursus lain! Lihat:
 
 ---
 
-### Azure / Edge / MCP / Agents
+### Azure / Edge / MCP / Agen
 [![AZD untuk Pemula](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 untuk Pemula](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 untuk Pemula](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)
-[![Ejen AI untuk Pemula](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)
+[![Agen AI untuk Pemula](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)
 
 ---
  
-### Siri AI Generatif
-[![AI Generatif untuk Pemula](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)
-[![AI Generatif (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)
-[![AI Generatif (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)
+### Siri Generatif AI
+[![Kecerdasan Buatan Generatif untuk Pemula](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)
+[![Kecerdasan Buatan Generatif (.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)
+[![Kecerdasan Buatan Generatif (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)
+[![Kecerdasan Buatan Generatif (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)
 
 ---
  
 ### Pembelajaran Teras
-[![ML untuk Pemula](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)
+[![Pembelajaran Mesin untuk Pemula](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)
 [![Sains Data untuk Pemula](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 untuk Pemula](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)
+[![Kecerdasan Buatan untuk Pemula](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)
 [![Keselamatan Siber untuk Pemula](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)
 [![Pembangunan Web untuk Pemula](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 untuk Pemula](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)
@@ -200,24 +209,24 @@ Pasukan kami menghasilkan kursus lain! Lihat:
 ---
  
 ### Siri Copilot
-[![Copilot untuk Pengaturcaraan Berpasangan AI](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 untuk Pemrograman Berpasangan AI](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 untuk 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)
 [![Pengembaraan Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
 ## Mendapatkan Bantuan
 
-Jika anda tersekat atau mempunyai sebarang soalan mengenai pembinaan aplikasi AI. Sertai pelajar lain dan pembangun berpengalaman dalam perbincangan tentang MCP. Ia adalah komuniti yang menyokong di mana soalan dialu-alukan dan pengetahuan dikongsi dengan bebas.
+Jika anda tersekat atau mempunyai sebarang soalan mengenai membina aplikasi AI. Sertai pembelajar lain dan pembangun berpengalaman dalam perbincangan mengenai MCP. Ia adalah komuniti yang menyokong di mana soalan dialu-alukan dan pengetahuan dikongsi secara bebas.
 
-[![Discord Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Jika anda mempunyai maklum balas produk atau ralat semasa membina, lawati:
+Jika anda mempunyai maklum balas produk atau menghadapi kesilapan ketika membina, kunjungi:
 
 [![Forum Pembangun Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-Penafian:
-Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi ralat atau ketidakakuratan. Dokumen asal dalam bahasa asalnya hendaklah dianggap sebagai sumber yang sah. Untuk maklumat penting, disyorkan terjemahan profesional oleh penterjemah manusia. Kami tidak bertanggungjawab terhadap sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.
+**Penafian**:
+Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk mencapai ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang sahih. Untuk maklumat penting, disarankan menggunakan terjemahan profesional oleh manusia. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/tl/README.md b/translations/tl/README.md
index f1365dc50..9c806b450 100644
--- a/translations/tl/README.md
+++ b/translations/tl/README.md
@@ -1,186 +1,195 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T12:49:28+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T14:12:53+00:00",
   "source_file": "README.md",
   "language_code": "tl"
 }
 -->
-[![Lisensya ng GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![Mga kontribyutor ng GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![Mga isyu ng GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![Mga pull request ng GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![Malugod ang PRs](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)
 
-[![Mga tagasubaybay ng GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![Mga fork ng GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![Mga bituin ng GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 ### 🌐 Suporta sa Maramihang Wika
 
-#### Sinusuportahan sa pamamagitan ng GitHub Action (Ina-automate at Laging Napapanahon)
+#### Sinusuportahan sa pamamagitan ng GitHub Action (Automatiko at Palaging Napapanahon)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabe](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgaro](../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) | [Kroatiyan](../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)](./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)
+[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)](./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)
+
+> **Mas gusto mo bang Mag-clone nang Lokal?**
+
+> Kasama sa repository na ito ang 50+ na pagsasalin ng wika na nagpapalaki nang malaki sa laki ng pag-download. Para mag-clone nang walang mga pagsasalin, gamitin ang 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'
+> ```
+> Binibigyan ka nito ng lahat ng kakailanganin mo para matapos ang kurso nang mas mabilis ang pag-download.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### Sumali sa Aming Komunidad
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Mayroon kaming serye sa Discord na "Learn with AI" na nagpapatuloy—alamin pa at sumali sa amin sa [Learn with AI Series](https://aka.ms/learnwithai/discord) mula 18 - 30 Setyembre, 2025. Makakakuha ka ng mga tip at trick sa paggamit ng GitHub Copilot para sa Data Science.
+Mayroon kaming patuloy na Discord learn with AI series, alamin pa at sumali sa amin sa [Learn with AI Series](https://aka.ms/learnwithai/discord) mula Setyembre 18 - 30, 2025. Makakakuha ka ng mga tip at trick sa paggamit ng GitHub Copilot para sa Data Science.
 
-![Serye ng Learn with AI](../../translated_images/tl/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/tl/3.9b58fd8d6c373c20.webp)
 
-# Machine Learning para sa mga Nagsisimula - Isang Kurikulum
+# Machine Learning para sa mga Baguhan - Isang Kurikulum
 
-> 🌍 Maglakbay sa paligid ng mundo habang tinutuklas natin ang Machine Learning sa pamamagitan ng mga kultura sa mundo 🌍
+> 🌍 Maglakbay sa buong mundo habang tinutuklasan natin ang Machine Learning gamit ang mga kultura ng mundo 🌍
 
-Ang Cloud Advocates sa Microsoft ay natutuwa na mag-alok ng isang 12-linggong, 26-araling kurikulum na tungkol sa **Machine Learning**. Sa kurikulum na ito, matututuhan mo ang kung ano ang tinatawag minsan na **classic machine learning**, gamit pangunahin ang Scikit-learn bilang library at iniiwasan ang deep learning, na sakop sa aming [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pagsamahin din ang mga leksyon na ito sa aming ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners).
+Ikinagagalak ng Cloud Advocates sa Microsoft na mag-alok ng 12-linggong kurikulum na may 26 na aralin na lahat tungkol sa **Machine Learning**. Sa kurikulum na ito, matututuhan mo ang tinatawag na **classic machine learning**, na gumagamit pangunahin ang Scikit-learn bilang library at iniiwasan ang deep learning, na tinatalakay sa aming [AI para sa mga Baguhan na kurikulum](https://aka.ms/ai4beginners). Pagsamahin ang mga araling ito sa aming ['Data Science para sa mga Baguhan' na kurikulum](https://aka.ms/ds4beginners), din!
 
-Maglakbay kasama namin sa iba't ibang panig ng mundo habang inaaplay namin ang mga tradisyunal na teknik na ito sa data mula sa maraming lugar sa mundo. Bawat aralin ay may kasamang pre- at post-lesson quizzes, nakasulat na mga tagubilin para tapusin ang aralin, isang solusyon, isang takdang-aralin, at iba pa. Ang aming project-based na pedagogiya ay nagpapahintulot sa iyo na matuto habang nagtatayo, isang napatunayang paraan para manatili ang mga bagong kasanayan.
+Maglakbay kasama namin sa buong mundo habang inaaplay namin ang mga klasikong teknik na ito sa datos mula sa maraming bahagi ng mundo. Bawat aralin ay may kasamang pre- at post-lesson quizzes, nakasulat na mga instruksyon para matapos ang aralin, solusyon, takdang-aralin, at iba pa. Ang aming proyekto-based na pedagohiya ay nagpapahintulot sa iyo na matuto habang gumagawa, isang napatunayang paraan para tumagal ang mga bagong kasanayan.
 
-**✍️ Taos-pusong pasasalamat sa aming mga may-akda** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ Taos-pusong pasasalamat sa aming mga may-akda** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu at Amy Boyd
 
-**🎨 Pasasalamat din sa aming mga illustrator** Tomomi Imura, Dasani Madipalli, at Jen Looper
+**🎨 Pasasalamat din sa aming mga ilustrador** Tomomi Imura, Dasani Madipalli, at Jen Looper
 
-**🙏 Espesyal na pasasalamat 🙏 sa aming mga Microsoft Student Ambassador na mga may-akda, reviewer, at kontribyutor ng nilalaman**, lalo na sina Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, at Snigdha Agarwal
+**🙏 Espesyal na pasasalamat 🙏 sa aming mga Microsoft Student Ambassador na mga awtor, tagasuri, at mga nag-ambag ng nilalaman**, lalo na kina Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, at Snigdha Agarwal
 
-**🤩 Dagdag na pasasalamat kina Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, at Vidushi Gupta para sa aming mga R na leksyon!**
+**🤩 Dagdag na pasasalamat sa mga Microsoft Student Ambassadors na sina Eric Wanjau, Jasleen Sondhi, at Vidushi Gupta para sa aming mga aralin sa R!**
 
 # Pagsisimula
 
 Sundin ang mga hakbang na ito:
-1. **I-fork ang Repository**: I-click ang pindutang "Fork" sa kanang-itaas ng pahinang ito.
+1. **I-fork ang Repository**: I-click ang button na "Fork" sa itaas na kanang bahagi ng pahinang ito.
 2. **I-clone ang Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [hanapin ang lahat ng karagdagang mga resources para sa kursong ito sa aming Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [hanapin ang lahat ng karagdagang mga mapagkukunan para sa kurso na ito sa aming Microsoft Learn na koleksyon](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Kailangan ng tulong?** Tingnan ang aming [Troubleshooting Guide](TROUBLESHOOTING.md) para sa mga solusyon sa karaniwang isyu sa pag-install, pagsasaayos, at pagpapatakbo ng mga leksyon.
+> 🔧 **Kailangan ng tulong?** Tingnan ang aming [Troubleshooting Guide](TROUBLESHOOTING.md) para sa mga solusyon sa mga karaniwang isyu sa pag-install, pagsasaayos, at pagpapatakbo ng mga aralin.
 
 
-**[Mga Estudyante](https://aka.ms/student-page)**, upang magamit ang kurikulum na ito, i-fork ang buong repo sa inyong sariling GitHub account at kumpletuhin ang mga ehersisyo nang mag-isa o kasama ng grupo:
+**[Mga mag-aaral](https://aka.ms/student-page)**, para gamitin ang kurikulum na ito, i-fork ang buong repo sa sarili mong GitHub account at tapusin ang mga ehersisyo nang mag-isa o kasama ang grupo:
 
 - Magsimula sa isang pre-lecture quiz.
-- Basahin ang lektyur at kumpletuhin ang mga aktibidad, huminto at magnilay sa bawat knowledge check.
-- Subukang likhain ang mga proyekto sa pamamagitan ng pagkaunawa sa mga aralin sa halip na direktang patakbuhin ang code ng solusyon; gayunpaman ang code na iyon ay makukuha sa mga `/solution` na folder sa bawat araling nakatuon sa proyekto.
-- Kunan ang post-lecture quiz.
-- Kumpletuhin ang hamon.
-- Kumpletuhin ang takdang-aralin.
-- Pagkatapos makumpleto ang isang grupo ng mga aralin, bisitahin ang [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) at "mag-aral nang malakas" sa pamamagitan ng pagpuno ng angkop na PAT rubric. Ang 'PAT' ay isang Progress Assessment Tool na isang rubric na pinupunan mo upang higit pang paunlarin ang iyong pagkatuto. Maaari ka ring mag-react sa ibang mga PAT upang sabay-sabay tayong matuto.
+- Basahin ang lektura at tapusin ang mga aktibidad, huminto at magnilay-nilay sa bawat knowledge check.
+- Subukang likhain ang mga proyekto sa pamamagitan ng pag-unawa sa mga aralin sa halip na patakbuhin ang solusyon na code; gayunpaman, ang code na iyon ay makukuha sa mga `/solution` na folder sa bawat araling nakatuon sa proyekto.
+- Kunin ang post-lecture quiz.
+- Tapusin ang hamon.
+- Tapusin ang takdang-aralin.
+- Pagkatapos matapos ang isang grupo ng aralin, bisitahin ang [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) at "matuto nang malakas" sa pamamagitan ng pagpuno sa naaangkop na PAT rubric. Ang 'PAT' ay isang Progress Assessment Tool na isang rubric na pinupunan mo para mapalawak ang iyong pagkatuto. Maaari ka ring mag-react sa iba pang PAT upang sabay-sabay tayong matuto.
 
-> Para sa karagdagang pag-aaral, inirerekomenda naming sundan ang mga [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) module at learning paths.
+> Para sa karagdagang pag-aaral, inirerekomenda naming sundan ang mga [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) na module at landas sa pagkatuto.
 
-**Mga Guro**, naglagay kami ng [ilang mungkahi](for-teachers.md) kung paano gamitin ang kurikulum na ito.
+**Mga guro**, mayroong kami [ilang suhestiyon](for-teachers.md) kung paano gamitin ang kurikulum na ito.
 
 ---
 
-## Mga walkthrough na video
+## Mga Video na Gabay
 
-Ang ilan sa mga leksyon ay available bilang maikling anyo ng video. Makikita mo ang lahat ng ito sa loob mismo ng mga leksyon, o sa [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) sa pamamagitan ng pag-click sa larawan sa ibaba.
+Ang ilang mga aralin ay available bilang maikling porma ng video. Makikita mo ang lahat ng ito nang naka-inline sa mga aralin, o sa [ML for Beginners playlist sa Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) sa pamamagitan ng pag-click sa larawan sa ibaba.
 
-[![ML for beginners banner](../../translated_images/tl/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/tl/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Kilalanin ang Koponan
+## Kilalanin ang Team
 
-[![Pampromosyong video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
 **Gif ni** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 I-click ang larawang nasa itaas para sa isang video tungkol sa proyekto at sa mga taong lumikha nito!
+> 🎥 I-click ang larawan sa itaas para sa isang video tungkol sa proyekto at mga taong lumikha nito!
 
 ---
 
-## Pedagohiya
+## Pedagogiya
 
-Pinili namin ang dalawang prinsipyo sa pagtuturo habang binubuo ang kurikulum na ito: tiyakin na ito ay hands-on at nakabatay sa proyekto (**project-based**) at na mayroon itong **madalas na mga pagsusulit**. Bilang karagdagan, ang kurikulum na ito ay may isang karaniwang **tema** upang magbigay ng pagkakaugnay-ugnay.
+Pinili namin ang dalawang pedagogical na prinsipyo habang ginagawa ang kurikulum na ito: pagtiyak na ito ay hands-on na **project-based** at na ito ay may kasamang mga **madalas na quizzes**. Bukod pa rito, ang kurikulum na ito ay may isang pangkalahatang **tema** upang bigyan ito ng pagkakaugnay-ugnay.
 
-Sa pamamagitan ng pagtiyak na ang nilalaman ay naka-align sa mga proyekto, nagiging mas kaakit-akit ang proseso para sa mga estudyante at mas napapaigting ang pag-alala ng mga konsepto. Bilang karagdagan, ang isang low-stakes quiz bago ang klase ay nagtatakda ng intensyon ng estudyante tungo sa pagkatuto ng isang paksa, habang ang pangalawang pagsusulit pagkatapos ng klase ay nagtutiyak ng karagdagang pag-alala. Ang kurikulum na ito ay idinisenyo upang maging flexible at masaya at maaaring kunin nang buo o kahit bahagya. Ang mga proyekto ay nagsisimula nang maliit at dahan-dahang nagiging mas kumplikado sa pagtatapos ng 12-linggong siklo. Kasama rin sa kurikulum na ito ang isang postscript tungkol sa mga aplikasyon ng ML sa totoong mundo, na maaaring gamitin bilang dagdag na kredito o bilang batayan para sa diskusyon.
+Sa pamamagitan ng pagtiyak na ang nilalaman ay naaayon sa mga proyekto, ang proseso ay nagiging mas kawili-wili para sa mga mag-aaral at ang pag-alala sa mga konsepto ay mapapalakas. Bukod pa rito, ang isang low-stakes na pagsusulit bago ang klase ay nagtatakda ng intensyon ng mag-aaral sa pag-aaral ng paksa, habang ang pangalawang pagsusulit pagkatapos ng klase ay nagsisiguro ng karagdagang pag-alala. Ang kurikulum na ito ay dinisenyo upang maging flexible at masaya at maaaring kunin nang buo o kalahati lamang. Nagsisimula ang mga proyekto sa maliit at nagiging mas komplikado sa katapusan ng 12-linggong siklo. Kabilang din sa kurikulum na ito ang isang postscript tungkol sa mga tunay na aplikasyon ng ML, na maaaring gamitin bilang dagdag na kredito o bilang batayan para sa diskusyon.
 
-> Hanapin ang aming [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), at [Troubleshooting](TROUBLESHOOTING.md) na mga gabay. Malugod naming tinatanggap ang iyong konstruktibong puna!
+> Hanapin ang aming [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), at [Troubleshooting](TROUBLESHOOTING.md) na mga gabay. Malugod naming tinatanggap ang inyong kapaki-pakinabang na puna!
 
-## Bawat aralin ay may kasamang
+## Kasama sa bawat aralin
 
 - opsyonal na sketchnote
-- opsyonal na karagdagang video
-- video walkthrough (ilan lamang sa mga aralin)
-- [paunang warmup na pagsusulit bago lektyur](https://ff-quizzes.netlify.app/en/ml/)
+- opsyonal na dagdag na video
+- video walkthrough (ilang aralin lamang)
+- [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
 - nakasulat na aralin
-- para sa mga araling nakabatay sa proyekto, mga gabay na hakbang-hakbang kung paano buuin ang proyekto
-- mga tsek sa kaalaman
+- para sa mga aralin na nakatuon sa proyekto, mga hakbang-hakbang na gabay kung paano buuin ang proyekto
+- knowledge checks
 - isang hamon
-- karagdagang babasahin
+- dagdag na babasahin
 - takdang-aralin
-- [pagsusulit pagkatapos ng lektyur](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Isang paalala tungkol sa mga wika**: Pangunahin na nakasulat ang mga leksyon na ito sa Python, ngunit marami rin ang available sa R. Upang kumpletuhin ang isang R na leksyon, pumunta sa `/solution` folder at hanapin ang mga R na leksyon. Sila ay may extension na .rmd na kumakatawan sa isang **R Markdown** file na maaaring simpleng ilarawan bilang isang pagsasanib ng `code chunks` (ng R o iba pang mga wika) at isang `YAML header` (na gumagabay kung paano i-format ang mga output tulad ng PDF) sa isang `Markdown document`. Bilang ganoon, nagsisilbi itong isang halimbawa ng framework sa pag-author para sa data science dahil pinapayagan kang pagsamahin ang iyong code, ang output nito, at ang iyong mga ideya sa pamamagitan ng pagsusulat ng mga ito sa Markdown. Higit pa rito, ang mga dokumentong R Markdown ay maaaring i-render sa mga output format tulad ng PDF, HTML, o Word.
+- [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
 
-> **Isang paalala tungkol sa mga pagsusulit**: Lahat ng pagsusulit ay nasa [Quiz App folder](../../quiz-app), para sa kabuuang 52 na pagsusulit na may tig-3 tanong bawat isa. Nakalink ang mga ito mula sa loob ng mga leksyon ngunit ang quiz app ay maaaring patakbuhin nang lokal; sundin ang mga tagubilin sa `quiz-app` folder upang i-host nang lokal o i-deploy sa Azure.
+> **Isang paalala tungkol sa mga wika**: Pangunahing nakasulat sa Python ang mga araling ito, ngunit marami rin ang available sa R. Para matapos ang isang aralin sa R, pumunta sa folder na `/solution` at hanapin ang mga aralin sa R. Kasama rito ang extension na .rmd na nangangahulugang isang **R Markdown** na file na simpleng tinukoy bilang isang pagsasama ng `code chunks` (ng R o iba pang mga wika) at isang `YAML header` (na gumagabay kung paano iformat ang mga output tulad ng PDF) sa isang `Markdown document`. Dahil dito, nagsisilbi itong isang halimbawa ng authoring framework para sa data science dahil pinapahintulutan kang pagsamahin ang iyong code, ang output nito, at ang iyong mga saloobin sa pamamagitan ng pagsusulat ng mga ito sa Markdown. Bukod dito, maaaring i-render ang mga dokumento ng R Markdown sa mga output format tulad ng PDF, HTML, o Word.
+> **Isang tala tungkol sa mga pagsusulit**: Lahat ng pagsusulit ay nakapaloob sa [Quiz App folder](../../quiz-app), para sa kabuuang 52 pagsusulit na may tig-tlong tanong bawat isa. Ito ay naka-link mula sa loob ng mga aralin ngunit ang quiz app ay pwedeng patakbuhin nang lokal; sundin ang mga tagubilin sa `quiz-app` folder upang mag-host nang lokal o mag-deploy sa Azure.
 
-| Numero ng Aralin |                             Paksa                              |                   Pag-uuri ng Aralin                   | Mga Layunin sa Pagkatuto                                                                                                             |                                                              Naka-link na Aralin                                                               |                        May-Akda                        |
+| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
 | :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Panimula sa machine learning                |      [Panimula](1-Introduction/README.md)       | Matutunan ang mga pangunahing konsepto sa likod ng machine learning                                                                                |                                             [Aralin](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Kasaysayan ng machine learning                 |      [Panimula](1-Introduction/README.md)       | Matutunan ang kasaysayan sa likod ng larangang ito                                                                                         |                                            [Aralin](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 Katarungan at machine learning                  |      [Panimula](1-Introduction/README.md)       | Ano ang mga mahahalagang pilosopikal na isyu tungkol sa katarungan na dapat isaalang-alang ng mga estudyante kapag bumubuo at nag-aaplay ng mga modelo ng ML? |                                              [Aralin](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Mga Teknik para sa machine learning                 |      [Panimula](1-Introduction/README.md)       | Anong mga teknik ang ginagamit ng mga mananaliksik ng ML para bumuo ng mga modelo?                                                                       |                                          [Aralin](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Panimula sa regresyon                   |        [Regresyon](2-Regression/README.md)         | Magsimula sa Python at Scikit-learn para sa mga modelo ng regresyon                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Presyo ng kalabasa sa Hilagang Amerika 🎃                |        [Regresyon](2-Regression/README.md)         | I-visualize at linisin ang datos bilang paghahanda para sa ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Presyo ng kalabasa sa Hilagang Amerika 🎃                |        [Regresyon](2-Regression/README.md)         | Bumuo ng linear at polynomial na mga modelo ng regresyon                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                Presyo ng kalabasa sa Hilagang Amerika 🎃                |        [Regresyon](2-Regression/README.md)         | Bumuo ng isang logistic regression na modelo                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Isang Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Bumuo ng web app para gamitin ang iyong sinanay na modelo                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Panimula sa klasipikasyon                 |    [Klasipikasyon](4-Classification/README.md)     | Linisin, ihanda, at i-visualize ang iyong datos; panimula sa klasipikasyon                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             Masasarap na pagkaing Asyano at Indiano 🍜             |    [Klasipikasyon](4-Classification/README.md)     | Panimula sa mga classifier                                                                                                     | [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       |             Masasarap na pagkaing Asyano at Indiano 🍜             |    [Klasipikasyon](4-Classification/README.md)     | Higit pang mga classifier                                                                                                                | [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       |             Masasarap na pagkaing Asyano at Indiano 🍜             |    [Klasipikasyon](4-Classification/README.md)     | Bumuo ng recommender web app gamit ang iyong modelo                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Panimula sa clustering                   |        [Clustering](5-Clustering/README.md)         | Linisin, ihanda, at i-visualize ang iyong datos; panimula sa clustering                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Pagtuklas sa mga Panlasa sa Musika ng Nigeria 🎧              |        [Clustering](5-Clustering/README.md)         | Suriin ang paraan ng K-Means clustering                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Panimula sa natural language processing ☕️         |   [Natural language processing](6-NLP/README.md)    | Matutunan ang mga pangunahing kaalaman sa NLP sa pamamagitan ng pagbuo ng isang simpleng bot                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Mga Karaniwang Gawain sa NLP ☕️                      |   [Natural language processing](6-NLP/README.md)    | Palalimin ang iyong kaalaman sa NLP sa pamamagitan ng pag-unawa sa mga karaniwang gawain na kailangan kapag humaharap sa mga istruktura ng wika                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Pagsasalin at sentiment analysis ♥️              |   [Natural language processing](6-NLP/README.md)    | Pagsasalin at pagsusuri ng damdamin gamit si Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Mga romantikong hotel sa Europa ♥️                  |   [Natural language processing](6-NLP/README.md)    | Pagsusuri ng damdamin gamit ang mga review ng hotel 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Mga romantikong hotel sa Europa ♥️                  |   [Natural language processing](6-NLP/README.md)    | Pagsusuri ng damdamin gamit ang mga review ng hotel 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Panimula sa time series forecasting             |        [Time series](7-TimeSeries/README.md)        | Panimula sa time series forecasting                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Paggamit ng Kuryente sa Mundo ⚡️ - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Pagtataya ng time series gamit ang ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Paggamit ng Kuryente sa Mundo ⚡️ - time series forecasting with SVR  |        [Time series](7-TimeSeries/README.md)        | Pagtataya ng time series gamit ang Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Panimula sa reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Panimula sa reinforcement learning gamit ang Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Tulungan si Peter iwasan ang lobo! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            Mga totoong sitwasyon at aplikasyon ng ML            |      [ML in the Wild](9-Real-World/README.md)       | Mga kawili-wili at nagsisilbing paglalarawan na aplikasyon sa totoong mundo ng klasikong ML                                                               |                                             [Aralin](9-Real-World/1-Applications/README.md)                                              |                         Koponan                         |
-|  Postscript   |            Pag-debug ng Modelo sa ML gamit ang RAI dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Pag-debug ng Modelo sa Machine Learning gamit ang mga komponent ng Responsible AI dashboard                                                              |                                             [Aralin](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [hanapin ang lahat ng karagdagang mapagkukunan para sa kursong ito sa aming koleksyon ng Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-## Offline na pag-access
-
-Maaari mong patakbuhin ang dokumentasyong ito nang offline gamit ang [Docsify](https://docsify.js.org/#/). I-fork ang repo na ito, [i-install ang Docsify](https://docsify.js.org/#/quickstart) sa iyong lokal na makina, at pagkatapos ay sa root na folder ng repo na ito, i-type `docsify serve`. Ang website ay ihahain sa port na 3000 sa iyong localhost: `localhost:3000`.
-
-## Mga PDF
-
-Makakahanap ng pdf ng kurikulum na may mga link [dito](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
-
-
-## 🎒 Iba pang Kurso 
-
-Ang aming koponan ay gumagawa ng iba pang mga kurso! Tingnan:
+|      01       |                Panimula sa machine learning                   |      [Introduction](1-Introduction/README.md)       | Matutunan ang mga pangunahing konsepto sa likod ng machine learning                                                            |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|      02       |                Kasaysayan ng machine learning                 |      [Introduction](1-Introduction/README.md)       | Matutunan ang kasaysayan sa likod ng larangang ito                                                                             |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|      03       |                 Katarungan at machine learning                |      [Introduction](1-Introduction/README.md)       | Ano ang mahahalagang isyung pilosopikal tungkol sa katarungan na dapat isaalang-alang ng mga estudyante sa pagbuo at aplikasyon ng ML models? |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|      04       |                Teknik para sa machine learning                |      [Introduction](1-Introduction/README.md)       | Anong mga teknik ang ginagamit ng mga mananaliksik sa ML para gumawa ng ML models?                                             |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|      05       |                   Panimula sa regression                      |        [Regression](2-Regression/README.md)         | Magsimula sa Python at Scikit-learn para sa mga regression model                                                             |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06       |                Presyo ng kalabasa sa Hilagang Amerika 🎃      |        [Regression](2-Regression/README.md)         | I-visualize at linisin ang datos bilang paghahanda sa ML                                                                       |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07       |                Presyo ng kalabasa sa Hilagang Amerika 🎃      |        [Regression](2-Regression/README.md)         | Gumawa ng linear at 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       |                Presyo ng kalabasa sa Hilagang Amerika 🎃      |        [Regression](2-Regression/README.md)         | Gumawa ng logistic regression model                                                                                            |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09       |                          Isang Web App 🔌                      |           [Web App](3-Web-App/README.md)            | Gumawa ng web app para gamitin ang iyong na-train na modelo                                                                    |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10       |                 Panimula sa classification                     |    [Classification](4-Classification/README.md)     | Linisin, ihanda, at i-visualize ang iyong datos; panimula sa classification                                                     | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|      11       |             Masasarap na pagkaing Asyano at Indian 🍜          |    [Classification](4-Classification/README.md)     | Panimula sa mga 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       |             Masasarap na pagkaing Asyano at Indian 🍜          |    [Classification](4-Classification/README.md)     | Higit pang mga 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       |             Masasarap na pagkaing Asyano at Indian 🍜          |    [Classification](4-Classification/README.md)     | Gumawa ng recommender web app gamit ang iyong modelo                                                                           |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14       |                   Panimula sa clustering                       |        [Clustering](5-Clustering/README.md)         | Linisin, ihanda, at i-visualize ang iyong datos; panimula sa clustering                                                        |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15       |              Pagsusuri ng mga musikang Nigerian 🎧             |        [Clustering](5-Clustering/README.md)         | Suriin ang 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       |        Panimula sa natural language processing ☕️             |   [Natural language processing](6-NLP/README.md)    | Matutunan ang mga batayan ng NLP sa paggawa ng isang simpleng bot                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17       |                      Karaniwang mga Gawain sa NLP ☕️          |   [Natural language processing](6-NLP/README.md)    | Palalimin ang iyong kaalaman sa NLP sa pamamagitan ng pag-unawa sa mga karaniwang gawain sa pagtatrabaho sa mga istruktura ng wika |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      18       |             Pagsasalin at pagsusuri ng damdamin ♥️             |   [Natural language processing](6-NLP/README.md)    | Pagsasalin at pagsusuri ng damdamin gamit si Jane Austen                                                                       |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|      19       |                  Mga romantikong hotel ng Europa ♥️             |   [Natural language processing](6-NLP/README.md)    | Pagsusuri ng damdamin gamit ang mga review ng hotel 1                                                                          |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|      20       |                  Mga romantikong hotel ng Europa ♥️             |   [Natural language processing](6-NLP/README.md)    | Pagsusuri ng damdamin gamit ang mga review ng hotel 2                                                                          |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|      21       |            Panimula sa time series forecasting                 |        [Time series](7-TimeSeries/README.md)        | Panimula sa time series forecasting                                                                                           |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      22       | ⚡️ Paggamit ng Kuryente sa Mundo ⚡️ - time series forecasting gamit ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting gamit ang ARIMA                                                                                       |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|      23       |  ⚡️ Paggamit ng Kuryente sa Mundo ⚡️ - time series forecasting gamit SVR   |        [Time series](7-TimeSeries/README.md)        | Time series forecasting gamit ang Support Vector Regressor                                                                     |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|      24       |             Panimula sa reinforcement learning                | [Reinforcement learning](8-Reinforcement/README.md) | Panimula sa reinforcement learning gamit ang Q-Learning                                                                        |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|      25       |                 Tulungan si Peter na iwasan ang lobo! 🐺       | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                    |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  Postscript   |            Mga totoong senaryo at aplikasyon ng ML            |      [ML in the Wild](9-Real-World/README.md)       | Mga kawili-wili at nagbibigay-liwanag na totoong aplikasyon ng klasikal na ML                                                  |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
+|  Postscript   |            Pag-debug ng Modelo sa ML gamit ang RAI dashboard |      [ML in the Wild](9-Real-World/README.md)       | Pag-debug ng Modelo sa Machine Learning gamit ang Responsible AI dashboard components                                           |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                   |
+
+> [hanapin lahat ng karagdagang mga resources para sa kursong ito sa aming Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
+## Offline access
+
+Maaari mong patakbuhin ang dokumentasyong ito offline gamit ang [Docsify](https://docsify.js.org/#/). I-fork ang repo na ito, [i-install ang Docsify](https://docsify.js.org/#/quickstart) sa iyong lokal na makina, at pagkatapos sa root folder ng repo na ito, i-type ang `docsify serve`. Ang website ay ise-serve sa port 3000 sa iyong localhost: `localhost:3000`.
+
+## PDFs
+
+Hanapin ang pdf ng kurikulum na may mga link [dito](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+
+
+## 🎒 Iba pang Mga Kurso 
+
+Ang aming koponan ay gumagawa ng iba pang mga kurso! Tignan:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j para sa mga Nagsisimula](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 para sa mga Nagsisimula](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 para sa mga Nagsisimula](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 para sa mga Nagsisimula](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 para sa mga Nagsisimula](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 para sa mga Nagsisimula](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)
 
 ---
  
-### Series ng Generative AI
+### Generative AI Series
 [![Generative AI para sa mga Nagsisimula](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)
@@ -188,7 +197,7 @@ Ang aming koponan ay gumagawa ng iba pang mga kurso! Tingnan:
 
 ---
  
-### Pangunahing Pag-aaral
+### Pangunahing Pagkatuto
 [![ML para sa mga Nagsisimula](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 para sa mga Nagsisimula](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 para sa mga Nagsisimula](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)
@@ -199,25 +208,25 @@ Ang aming koponan ay gumagawa ng iba pang mga kurso! Tingnan:
 
 ---
  
-### Serye ng Copilot
+### Copilot Series
 [![Copilot para sa 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 para sa 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)
-[![Pakikipagsapalaran ng Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot 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 -->
 
 ## Pagkuha ng Tulong
 
-Kung ma-stuck ka o may anumang mga tanong tungkol sa paggawa ng mga AI app. Sumali sa kapwa mga nag-aaral at mga bihasang developer sa mga talakayan tungkol sa MCP. Ito ay isang suportadong komunidad kung saan malugod ang mga tanong at malayang ibinabahagi ang kaalaman.
+Kung ikaw ay naiipit o may mga katanungan tungkol sa paggawa ng mga AI app, sumali sa mga kapwa nag-aaral at mga bihasang developer sa mga talakayan tungkol sa MCP. Isa itong suportadong komunidad kung saan malugod tinatanggap ang mga tanong at malayang naibabahagi ang kaalaman.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Kung mayroon kang feedback tungkol sa produkto o mga error habang nagtatayo, bisitahin:
+Kung mayroon kang puna sa produkto o mga error habang nagbuo, bisitahin:
 
 [![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 -->
-Paunawa:
-Isinalin ang dokumentong ito gamit ang AI na serbisyo sa pagsasalin na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagaman nagsusumikap kami para sa kawastuhan, pakatandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o kamalian. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na pangunahing pinagmumulan. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasaling-tao. Hindi kami mananagot para sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.
+**Paalala**:  
+Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Habang aming pinagsisikapang maging tumpak ang pagsasalin, pakatandaan na ang awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o di-katiyakan. Ang orihinal na dokumento sa sariling wika nito ang dapat ituring na pangunahing sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na nagmumula sa paggamit ng pagsasaling ito.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file