diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index db1c72b1..4e73a482 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -4,7 +4,7 @@ > 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) ### Introduction @@ -96,7 +96,7 @@ In the near future, understanding the basics of machine learning is going to be Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) ## Review & Self Study diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md index d8915f58..90798160 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.fr.md +++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md @@ -4,7 +4,7 @@ > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning. -## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=fr) +## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=fr) ### Introduction @@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre. -## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=fr) +## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=fr) ## Révision et auto-apprentissage diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md index d0daadd8..69a9157b 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.id.md +++ b/1-Introduction/1-intro-to-ML/translations/README.id.md @@ -4,7 +4,7 @@ > 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning. -## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/) +## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) ### Pengantar @@ -96,7 +96,7 @@ Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keha Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik. -## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/) +## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) ## Ulasan & Belajar Mandiri diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md index 7d9fe43a..715a9c90 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.it.md +++ b/1-Introduction/1-intro-to-ML/translations/README.it.md @@ -4,7 +4,7 @@ > 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) ### Introduzione @@ -97,7 +97,7 @@ Nel prossimo futuro, comprendere le basi di machine learning sarà un must per l Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) ## Revisione e Auto Apprendimento diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md index fd3c11d1..b88738d0 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ja.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md @@ -4,7 +4,7 @@ > 🎥 上の画像をクリックすると、機械学習、AI、深層学習の違いについて説明した動画が表示されます。 -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=ja) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ja) ### イントロダクション @@ -94,7 +94,7 @@ ## 🚀 Challenge AI、ML、深層学習、データサイエンスの違いについて理解していることを、紙や[Excalidraw](https://excalidraw.com/)などのオンラインアプリを使ってスケッチしてください。また、それぞれの技術が得意とする問題のアイデアを加えてみてください。 -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=ja) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja) ## 振り返りと自習 diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md index cfa7e127..669e649d 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.tr.md +++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md @@ -4,7 +4,7 @@ > 🎥 Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın. -## [Ders öncesi sınav](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=tr) +## [Ders öncesi sınav](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=tr) ### Introduction @@ -103,7 +103,7 @@ Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin tem Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin. -## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=tr) +## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=tr) ## İnceleme ve Bireysel Çalışma diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md index 45ec79be..3e8ae621 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md +++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md @@ -4,7 +4,7 @@ > 🎥 点击上面的图片观看讨论机器学习、人工智能和深度学习之间区别的视频。 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) ### 介绍 @@ -96,7 +96,7 @@ 在纸上或使用[Excalidraw](https://excalidraw.com/)等在线应用程序绘制草图，了解你对AI、ML、深度学习和数据科学之间差异的理解。添加一些关于这些技术擅长解决的问题的想法。 -## [阅读后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/) +## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) ## 复习与自学 diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md index 67c93dbf..a44703d6 100644 --- a/1-Introduction/2-history-of-ML/README.md +++ b/1-Introduction/2-history-of-ML/README.md @@ -3,7 +3,7 @@ ![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png) > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence. @@ -101,7 +101,7 @@ It remains to be seen what the future holds, but it is important to understand t Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) ## Review & Self Study diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md index c5154176..28402267 100755 --- a/1-Introduction/2-history-of-ML/translations/README.es.md +++ b/1-Introduction/2-history-of-ML/translations/README.es.md @@ -3,7 +3,7 @@ ![Resumen de la historoia del machine learning en un boceto](../../sketchnotes/ml-history.png) > Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial. @@ -102,7 +102,7 @@ Queda por ver qué depara el futuro, pero es importante entender estos sistemas Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se creó ningún descubrimiento científico en un vacío cultural. ¿Qué descubres? -## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/) +## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) ## Revisión y autoestudio diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md index 9c59eb6f..efe26877 100644 --- a/1-Introduction/2-history-of-ML/translations/README.fr.md +++ b/1-Introduction/2-history-of-ML/translations/README.fr.md @@ -3,7 +3,7 @@ ![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png) > Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=fr) +## [Quizz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=fr) Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle. @@ -102,7 +102,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ? -## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=fr) +## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=fr) ## Révision et auto-apprentissage diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md index 5e6a6f0f..9e695a8a 100644 --- a/1-Introduction/2-history-of-ML/translations/README.id.md +++ b/1-Introduction/2-history-of-ML/translations/README.id.md @@ -3,7 +3,7 @@ ![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-history.png) > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence. @@ -101,7 +101,7 @@ Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan? -## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/) +## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) ## Ulasan & Belajar Mandiri diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md index c7e59fd7..c7d5ce23 100644 --- a/1-Introduction/2-history-of-ML/translations/README.it.md +++ b/1-Introduction/2-history-of-ML/translations/README.it.md @@ -3,7 +3,7 @@ ![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png) > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale. @@ -103,7 +103,7 @@ Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi i Approfondire uno di questi momenti storici e scoprire di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) ## Revisione e Auto Apprendimento diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md index 5c17650c..6ba32096 100644 --- a/1-Introduction/2-history-of-ML/translations/README.ja.md +++ b/1-Introduction/2-history-of-ML/translations/README.ja.md @@ -3,7 +3,7 @@ ![機械学習の歴史をまとめたスケッチ](../../../sketchnotes/ml-history.png) > [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=ja) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ja) この授業では、機械学習と人工知能の歴史における主要な出来事を紹介します。 @@ -99,7 +99,7 @@ これらの歴史的瞬間の1つを掘り下げて、その背後にいる人々について学びましょう。魅力的な人々がいますし、文化的に空白の状態で科学的発見がなされたことはありません。どういったことが見つかるでしょうか？ -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=ja) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja) ## 振り返りと自習 diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md index a67f45ec..af2346fb 100644 --- a/1-Introduction/2-history-of-ML/translations/README.tr.md +++ b/1-Introduction/2-history-of-ML/translations/README.tr.md @@ -3,7 +3,7 @@ ![Bir taslak-notta makine öğrenimi geçmişinin özeti](../../../sketchnotes/ml-history.png) > [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not -## [Ders öncesi test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=tr) +## [Ders öncesi test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=tr) Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz. @@ -102,7 +102,7 @@ Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sisteml Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz? -## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=tr) +## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=tr) ## İnceleme ve Bireysel Çalışma diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md index 8ca7e690..70e85f82 100644 --- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md +++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md @@ -3,7 +3,7 @@ ![机器学习历史概述](../../../sketchnotes/ml-history.png) > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac) -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) 在本课中，我们将走过机器学习和人工智能历史上的主要里程碑。 @@ -101,7 +101,7 @@ Alan Turing，一个真正杰出的人，[在2019年被公众投票选出](https 深入了解这些历史时刻之一，并更多地了解它们背后的人。这里有许多引人入胜的人物，没有一项科学发现是在文化真空中创造出来的。你发现了什么？ -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) ## 复习与自学 diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index 79bf4819..7e9c8f6d 100644 --- a/1-Introduction/3-fairness/README.md +++ b/1-Introduction/3-fairness/README.md @@ -3,7 +3,7 @@ ![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png) > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) ## Introduction @@ -184,7 +184,7 @@ To prevent biases from being introduced in the first place, we should: Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) ## Review & Self Study In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning. diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md index 6f09a148..980cbd88 100644 --- a/1-Introduction/3-fairness/translations/README.id.md +++ b/1-Introduction/3-fairness/translations/README.id.md @@ -3,7 +3,7 @@ ![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-fairness.png) > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) +## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) ## Pengantar @@ -90,11 +90,11 @@ Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunj ✅ **Diskusi**: Tinjau kembali beberapa contoh dan lihat apakah mereka menunjukkan bahaya yang berbeda. -| | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan | -| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: | -| Sistem perekrutan otomatis | x | x | x | | x | -| Terjemahan mesin | | | | | | -| Melabeli foto | | | | | | +| | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan | +| -------------------------- | :-----: | :--------------: | :-------: | :----: | :--------------------------------------: | +| Sistem perekrutan otomatis | x | x | x | | x | +| Terjemahan mesin | | | | | | +| Melabeli foto | | | | | | ## Mendeteksi Ketidakadilan @@ -185,7 +185,7 @@ Untuk mencegah kemunculan bias pada awalnya, kita harus: Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan? -## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) +## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) ## Ulasan & Belajar Mandiri Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin. diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md index 3c167f09..e55fd6e7 100644 --- a/1-Introduction/3-fairness/translations/README.it.md +++ b/1-Introduction/3-fairness/translations/README.it.md @@ -3,7 +3,7 @@ ![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png) > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) ## Introduzione @@ -89,11 +89,11 @@ Questi cinque principali tipi di danno non si escludono a vicenda e un singolo s ✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi. -| | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione | -| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: | -| Sistema di assunzione automatizzato | x | x | x | | x | -| Traduzione automatica | | | | | | -| Eitchettatura foto | | | | | | +| | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione | +| ----------------------------------- | :---------: | :-----------------: | :--------: | :----------: | :----------------------------: | +| Sistema di assunzione automatizzato | x | x | x | | x | +| Traduzione automatica | | | | | | +| Eitchettatura foto | | | | | | ## Rilevare l'ingiustizia @@ -137,11 +137,11 @@ Si sono identificati i danni e un gruppo interessato, in questo caso, delineato ✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice -| | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio | -| ---------- | ------------------- | ------------------- | ----- | -| Donna | 0,37 | 0,27 | 54032 | -| Uomo | 0,31 | 0.35 | 28620 | -| Non binario | 0,33 | 0,31 | 1266 | +| | percentuale di falsi positivi | Percentuale di falsi negativi | conteggio | +| ----------- | ----------------------------- | ----------------------------- | --------- | +| Donna | 0,37 | 0,27 | 54032 | +| Uomo | 0,31 | 0.35 | 28620 | +| Non binario | 0,33 | 0,31 | 1266 | Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano questi numeri. @@ -183,7 +183,7 @@ Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe: Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) ## Revisione e Auto Apprendimento diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md index 9bb32639..ffa878c1 100644 --- a/1-Introduction/3-fairness/translations/README.ja.md +++ b/1-Introduction/3-fairness/translations/README.ja.md @@ -3,7 +3,7 @@ ![機械学習における公平性をまとめたスケッチ](../../../sketchnotes/ml-fairness.png) > [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5?loc=ja) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja) ## イントロダクション @@ -88,11 +88,11 @@ AIや機械学習における公平性の保証は、依然として複雑な社 ✅ **ディスカッション**: いくつかの例を再検討し、異なる害を示しているかどうかを確認してください。 -| | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 | -| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: | -| 採用システムの自動化 | x | x | x | | x | -| 機械翻訳 | | | | | | -| 写真のラベリング | | | | | | +| | アロケーション | サービスの質 | 固定観念 | 誹謗中傷 | 過剰表現/過小表現 | +| -------------------- | :------------: | :----------: | :------: | :------: | :---------------: | +| 採用システムの自動化 | x | x | x | | x | +| 機械翻訳 | | | | | | +| 写真のラベリング | | | | | | ## 不公平の検出 @@ -134,11 +134,11 @@ AIや機械学習における公平性の保証は、依然として複雑な社 ✅ 今後の"クラスタリング"のレッスンでは、この"混同行列"をコードで構築する方法をご紹介します。 -| | 偽陽性率 | 偽陰性率 | サンプル数 | -| ---------- | ------------------- | ------------------- | ----- | -| 女性 | 0.37 | 0.27 | 54032 | -| 男性 | 0.31 | 0.35 | 28620 | -| どちらにも属さない | 0.33 | 0.31 | 1266 | +| | 偽陽性率 | 偽陰性率 | サンプル数 | +| ------------------ | -------- | -------- | ---------- | +| 女性 | 0.37 | 0.27 | 54032 | +| 男性 | 0.31 | 0.35 | 28620 | +| どちらにも属さない | 0.33 | 0.31 | 1266 | この表から、いくつかのことがわかります。まず、データに含まれる男性と女性どちらでもない人が比較的少ないことがわかります。従ってこのデータは歪んでおり、この数字をどう解釈するかに注意が必要です。 @@ -178,7 +178,7 @@ AIや機械学習における公平性の保証は、依然として複雑な社 モデルの構築や使用において、不公平が明らかになるような現実のシナリオを考えてみてください。他にどのようなことを考えるべきでしょうか？ -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6?loc=ja) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ja) ## Review & Self Study このレッスンでは、機械学習における公平、不公平の概念の基礎を学びました。 diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md index 22204544..952e8191 100644 --- a/1-Introduction/3-fairness/translations/README.zh-cn.md +++ b/1-Introduction/3-fairness/translations/README.zh-cn.md @@ -3,7 +3,7 @@ ![机器学习中的公平性概述](../../../sketchnotes/ml-fairness.png) > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac) -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) ## 介绍 @@ -186,7 +186,7 @@ 想想现实生活中的场景，在模型构建和使用中明显存在不公平。我们还应该考虑什么？ -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) ## 复习与自学 在本课中，你学习了机器学习中公平和不公平概念的一些基础知识。 diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md index 70b96000..1b87fd7c 100644 --- a/1-Introduction/4-techniques-of-ML/README.md +++ b/1-Introduction/4-techniques-of-ML/README.md @@ -5,7 +5,7 @@ The process of building, using, and maintaining machine learning models and the - Understand the processes underpinning machine learning at a high level. - Explore base concepts such as 'models', 'predictions', and 'training data'. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) ## Introduction @@ -103,7 +103,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) ## Review & Self Study diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md index 114d6e8d..0121527e 100755 --- a/1-Introduction/4-techniques-of-ML/translations/README.es.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md @@ -6,7 +6,7 @@ El proceso de creación, uso y mantenimiento de modelos de machine learning, y l - Explorar conceptos básicos como 'modelos', 'predicciones', y 'datos de entrenamiento' -## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) ## Introducción A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos: @@ -101,7 +101,7 @@ En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, constr Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil? -## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) ## Revisión & Autoestudio diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md index 8e6fac43..69960370 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.id.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md @@ -5,7 +5,7 @@ Proses membangun, menggunakan, dan memelihara model machine learning dan data ya - Memahami gambaran dari proses yang mendasari machine learning. - Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'. -## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) ## Pengantar Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah: @@ -100,7 +100,7 @@ Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu? -## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) ## Ulasan & Belajar Mandiri diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md index 4e36c9cc..ecfb35e5 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.it.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md @@ -5,7 +5,7 @@ Il processo di creazione, utilizzo e mantenimento dei modelli di machine learnin - Comprendere i processi ad alto livello alla base di machine learning. - Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento". -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) ## Introduzione @@ -103,7 +103,7 @@ In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, co Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) ## Revisione e Auto Apprendimento diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md index b964781d..13dcdb31 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md @@ -5,7 +5,7 @@ - 機械学習を支えるプロセスを高い水準で理解します。 - 「モデル」「予測」「訓練データ」などの基本的な概念を調べます。 -## [講義前の小テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7?loc=ja) +## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ja) ## 導入 @@ -103,7 +103,7 @@ 機械学習の学習者のステップを反映したフローチャートを描いてください。今の自分はこのプロセスのどこにいると思いますか？どこに困難があると予想しますか？あなたにとって簡単そうなことは何ですか？ -## [講義後の小テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8?loc=ja) +## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ja) ## 振り返りと自主学習 diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md index c7b86c8e..2590b99a 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md @@ -6,7 +6,7 @@ - 在高层次上理解支持机器学习的过程。 - 探索基本概念，例如“模型”、“预测”和“训练数据”。 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) ## 介绍 在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤： @@ -101,7 +101,7 @@ 画一个流程图，反映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会遇到困难？什么对你来说很容易？ -## [阅读后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/) +## [阅读后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) ## 复习与自学 diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index 69986fba..275e79f5 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -4,7 +4,7 @@ > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) > ### [This lesson is available in R!](./solution/lesson_1-R.ipynb) @@ -199,7 +199,7 @@ Congratulations, you built your first linear regression model, created a predict ## 🚀Challenge Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) ## Review & Self Study diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md index b7ab4bf8..87b2be53 100644 --- a/2-Regression/1-Tools/translations/README.id.md +++ b/2-Regression/1-Tools/translations/README.id.md @@ -4,7 +4,7 @@ > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Kuis Pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) +## [Kuis Pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) ## Pembukaan Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai! @@ -195,7 +195,7 @@ Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah pre ## Tantangan Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit? -## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) +## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) ## Review & Pembelajaran Mandiri diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md index 48c61d34..a5e2e384 100644 --- a/2-Regression/1-Tools/translations/README.it.md +++ b/2-Regression/1-Tools/translations/README.it.md @@ -4,7 +4,7 @@ > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Qui Pre-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) +## [Qui Pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) ## Introduzione @@ -197,7 +197,7 @@ Congratulazioni, si è costruito il primo modello di regressione lineare, creato Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia? -## [Qui post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) +## [Qui post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) ## Riepilogo e Auto Apprendimento diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md index 0ecc0e82..fcb5ce0f 100644 --- a/2-Regression/1-Tools/translations/README.ja.md +++ b/2-Regression/1-Tools/translations/README.ja.md @@ -4,7 +4,7 @@ > [Tomomi Imura](https://www.twitter.com/girlie_mac) によって制作されたスケッチノート -## [講義前クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9?loc=ja) +## [講義前クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja) ## イントロダクション @@ -205,7 +205,7 @@ s1 tc: T細胞（白血球の一種） ## 🚀チャレンジ このデータセットから別の変数を選択してプロットしてください。ヒント： `X = X[:, np.newaxis, 2]` の行を編集する。今回のデータセットのターゲットである、糖尿病という病気の進行について、どのような発見があるのでしょうか？ -## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10?loc=ja) +## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja) ## レビュー & 自主学習 diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md index bb7f37c1..019eb06f 100644 --- a/2-Regression/1-Tools/translations/README.zh-cn.md +++ b/2-Regression/1-Tools/translations/README.zh-cn.md @@ -4,7 +4,7 @@ > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac) -## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/) +## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) ## 介绍 在这四节课中，你将了解如何构建回归模型。我们将很快讨论这些是什么。但在你做任何事情之前，请确保你有合适的工具来开始这个过程！ @@ -192,7 +192,7 @@ s1 tc：T细胞（一种白细胞） ## 🚀挑战 从这个数据集中绘制一个不同的变量。提示：编辑这一行：`X = X[:, np.newaxis, 2]`。鉴于此数据集的目标，你能够发现糖尿病作为一种疾病的进展情况吗？ -## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/) +## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) ## 复习与自学 diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index 56493188..a647eb6b 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -4,7 +4,7 @@ Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) > ### [This lesson is available in R!](./solution/lesson_2-R.ipynb) @@ -192,7 +192,7 @@ To get charts to display useful data, you usually need to group the data somehow Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) ## Review & Self Study diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md index b9889ae8..9d8b5f18 100644 --- a/2-Regression/2-Data/translations/README.id.md +++ b/2-Regression/2-Data/translations/README.id.md @@ -3,7 +3,7 @@ ![Infografik visualisasi data](../images/data-visualization.png) > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) +## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) ## Pembukaan @@ -191,7 +191,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi? -## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) +## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) ## Review & Pembelajaran Mandiri diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md index 7b78ac52..ba57ee11 100644 --- a/2-Regression/2-Data/translations/README.it.md +++ b/2-Regression/2-Data/translations/README.it.md @@ -3,7 +3,7 @@ > ![Infografica sulla visualizzazione dei dati](../images/data-visualization.png) > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) ## Introduzione @@ -190,7 +190,7 @@ Per fare in modo che i grafici mostrino dati utili, di solito è necessario ragg Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono più appropriati per i problemi di regressione? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) ## Revisione e Auto Apprendimento diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md index 8a9f4d55..ddd01a77 100644 --- a/2-Regression/2-Data/translations/README.ja.md +++ b/2-Regression/2-Data/translations/README.ja.md @@ -4,7 +4,7 @@ > > [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック -## [講義前のクイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11?loc=ja) +## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ja) ## イントロダクション @@ -195,7 +195,7 @@ Jupyter notebookでうまく利用できるテータ可視化ライブラリの Matplotlibが提供する様々なタイプのビジュアライゼーションを探ってみましょう。回帰の問題にはどのタイプが最も適しているでしょうか？ -## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12?loc=ja) +## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja) ## レビュー & 自主学習 diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md index bc273ab1..df542b15 100644 --- a/2-Regression/2-Data/translations/README.zh-cn.md +++ b/2-Regression/2-Data/translations/README.zh-cn.md @@ -3,7 +3,7 @@ > ![数据可视化信息图](../images/data-visualization.png) > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded) -## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/) +## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) ## 介绍 @@ -191,7 +191,7 @@ 探索Matplotlib提供的不同类型的可视化。哪种类型最适合回归问题？ -## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/) +## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) ## 复习与自学 diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 2a60658b..0a4e09b4 100644 --- a/2-Regression/3-Linear/README.md +++ b/2-Regression/3-Linear/README.md @@ -2,7 +2,7 @@ ![Linear vs polynomial regression infographic](./images/linear-polynomial.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) ### Introduction So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. @@ -320,7 +320,7 @@ It does make sense, given the plot! And, if this is a better model than the prev Test several different variables in this notebook to see how correlation corresponds to model accuracy. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## Review & Self Study diff --git a/2-Regression/3-Linear/solution/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/lesson_3-R.ipynb index 0cc040bb..09fadc99 100644 --- a/2-Regression/3-Linear/solution/lesson_3-R.ipynb +++ b/2-Regression/3-Linear/solution/lesson_3-R.ipynb @@ -1058,7 +1058,7 @@ "\n", "Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n", "\n", - "## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)\n", + "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n", "\n", "## **Review & Self Study**\n", "\n", diff --git a/2-Regression/3-Linear/solution/lesson_3.Rmd b/2-Regression/3-Linear/solution/lesson_3.Rmd index a1b81780..91968a06 100644 --- a/2-Regression/3-Linear/solution/lesson_3.Rmd +++ b/2-Regression/3-Linear/solution/lesson_3.Rmd @@ -662,7 +662,7 @@ The `polynomial model` prediction does make sense, given the scatter plots of `p Test several different variables in this notebook to see how correlation corresponds to model accuracy. -## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## **Review & Self Study** diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md index f2ae6b7c..ce2ee409 100644 --- a/2-Regression/3-Linear/translations/README.id.md +++ b/2-Regression/3-Linear/translations/README.id.md @@ -2,7 +2,7 @@ ![Infografik regresi linear vs polinomial](../images/linear-polynomial.png) > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) +## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) ### Pembukaan Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib. @@ -324,7 +324,7 @@ Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model. -## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## Review & Pembelajaran Mandiri diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md index 1aafa601..81fd5a11 100644 --- a/2-Regression/3-Linear/translations/README.it.md +++ b/2-Regression/3-Linear/translations/README.it.md @@ -3,7 +3,7 @@ ![Infografica di regressione lineare e polinomiale](../images/linear-polynomial.png) > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) ### Introduzione @@ -328,7 +328,7 @@ Ben fatto! Sono stati creati due modelli di regressione in una lezione. Nella s Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## Revisione e Auto Apprendimento diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md index cc3c3b7b..2dbc0f32 100644 --- a/2-Regression/3-Linear/translations/README.ja.md +++ b/2-Regression/3-Linear/translations/README.ja.md @@ -2,7 +2,7 @@ ![線形回帰 vs 多項式回帰 のインフォグラフィック](../images/linear-polynomial.png) > [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック -## [講義前のクイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) +## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) ### イントロダクション これまで、このレッスンで使用するカボチャの価格データセットから集めたサンプルデータを使って、回帰とは何かを探ってきました。また、Matplotlibを使って可視化を行いました。 @@ -323,7 +323,7 @@ Scikit-learnには、多項式回帰モデルを構築するための便利なAP このノートブックでいくつかの異なる変数をテストし、相関関係がモデルの精度にどのように影響するかを確認してみてください。 -## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## レビュー & 自主学習 diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md index 55c4d293..12b57103 100644 --- a/2-Regression/3-Linear/translations/README.zh-cn.md +++ b/2-Regression/3-Linear/translations/README.zh-cn.md @@ -2,7 +2,7 @@ ![线性与多项式回归信息图](../images/linear-polynomial.png) > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded) -## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/) +## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) ### 介绍 到目前为止，你已经通过从我们将在本课程中使用的南瓜定价数据集收集的样本数据探索了什么是回归。你还使用Matplotlib对其进行了可视化。 @@ -321,7 +321,7 @@ Scikit-learn包含一个用于构建多项式回归模型的有用API - `make_pi 在此notebook中测试几个不同的变量，以查看相关性与模型准确性的对应关系。 -## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/) +## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) ## 复习与自学 diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index a2a938a7..afc9c629 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -2,7 +2,7 @@ ![Logistic vs. linear regression infographic](./images/logistic-linear.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) ## Introduction @@ -222,10 +222,10 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels. -| |0|1| -|:-:|:-:|:-:| -|0|TN|FP| -|1|FN|TP| +| | 0 | 1 | +| :---: | :---: | :---: | +| 0 | TN | FP | +| 1 | FN | TP | What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'. @@ -296,7 +296,7 @@ In future lessons on classifications, you will learn how to iterate to improve y There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) ## Review & Self Study diff --git a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb index c9e16ce9..574cec05 100644 --- a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb +++ b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb @@ -45,7 +45,7 @@ { "cell_type": "markdown", "source": [ - "#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**\n", + "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n", "\n", "#### Introduction\n", "\n", diff --git a/2-Regression/4-Logistic/solution/lesson_4.Rmd b/2-Regression/4-Logistic/solution/lesson_4.Rmd index 8d99dc62..e974594d 100644 --- a/2-Regression/4-Logistic/solution/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/lesson_4.Rmd @@ -14,7 +14,7 @@ output: ![Infographic by Dasani Madipalli](../images/logistic-linear.png){width="600"} -#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)** +#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)** #### Introduction diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md index 213ef4aa..553205d7 100644 --- a/2-Regression/4-Logistic/translations/README.id.md +++ b/2-Regression/4-Logistic/translations/README.id.md @@ -3,7 +3,7 @@ ![Infografik regresi logistik vs. linear](../images/logistic-linear.png) > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/) +## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) ## Pembukaan @@ -291,7 +291,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik. -## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) +## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) ## Review & Pembelajaran mandiri diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md index e9940203..deb51eaa 100644 --- a/2-Regression/4-Logistic/translations/README.it.md +++ b/2-Regression/4-Logistic/translations/README.it.md @@ -3,7 +3,7 @@ ![Infografica di regressione lineare e logistica](../images/logistic-linear.png) > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) ## Introduzione @@ -284,7 +284,7 @@ Nelle lezioni future sulle classificazioni si imparerà come eseguire l'iterazio C'è molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare è sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si è appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) ## Revisione e Auto Apprendimento diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md index 3e384824..662a1eaf 100644 --- a/2-Regression/4-Logistic/translations/README.ja.md +++ b/2-Regression/4-Logistic/translations/README.ja.md @@ -2,7 +2,7 @@ ![ロジスティク回帰 vs 線形回帰のインフォグラフィック](../images/logistic-linear.png) > [Dasani Madipalli](https://twitter.com/dasani_decoded) によるインフォグラフィック -## [講義前のクイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/) +## [講義前のクイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) ## イントロダクション @@ -225,10 +225,10 @@ Seabornプロットでは、変数を並べて表示することができます Scikit-learnでは、混同行列の行 (axis=0)が実際のラベル、列 (axis=1)が予測ラベルとなります。 -| |0|1| -|:-:|:-:|:-:| -|0|TN|FP| -|1|FN|TP| +| | 0 | 1 | +| :---: | :---: | :---: | +| 0 | TN | FP | +| 1 | FN | TP | ここで何が起こっているのか？例えば、カボチャを「オレンジ色」と「オレンジ色でない」という2つのカテゴリーに分類するように求められたとしましょう。 @@ -299,7 +299,7 @@ print(auc) ロジスティック回帰については、まだまだ解き明かすべきことがたくさんあります。しかし、学ぶための最良の方法は、実験することです。この種の分析に適したデータセットを見つけて、それを使ってモデルを構築してみましょう。ヒント：面白いデータセットを探すために[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) を試してみてください。 -## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) +## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) ## レビュー & 自主学習 diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md index 5de169fb..dc1bb5e1 100644 --- a/2-Regression/4-Logistic/translations/README.zh-cn.md +++ b/2-Regression/4-Logistic/translations/README.zh-cn.md @@ -2,7 +2,7 @@ ![逻辑与线性回归信息图](../images/logistic-linear.png) > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded) -## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/) +## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) ## 介绍 @@ -282,7 +282,7 @@ print(auc) 关于逻辑回归，还有很多东西需要解开！但最好的学习方法是实验。找到适合此类分析的数据集并用它构建模型。你学到了什么？小贴士：尝试[Kaggle](https://kaggle.com)获取有趣的数据集。 -## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/) +## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) ## 复习与自学 diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index fae04026..c983e679 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you To do this, you need to build a web app using Flask. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) ## Building an app @@ -334,7 +334,7 @@ In a professional setting, you can see how good communication is necessary betwe Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) ## Review & Self Study diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md index 82454852..f31eb2d4 100644 --- a/3-Web-App/1-Web-App/translations/README.it.md +++ b/3-Web-App/1-Web-App/translations/README.it.md @@ -11,7 +11,7 @@ Si continuerà a utilizzare il notebook per pulire i dati e addestrare il modell Per fare ciò, è necessario creare un'app Web utilizzando Flask. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) ## Costruire un'app @@ -334,7 +334,7 @@ In un ambiente professionale, si può vedere quanto sia necessaria una buona com Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) ## Revisione e Auto Apprendimento diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md index edfaa342..b23050dd 100644 --- a/3-Web-App/1-Web-App/translations/README.ja.md +++ b/3-Web-App/1-Web-App/translations/README.ja.md @@ -11,7 +11,7 @@ そのためには、Flaskを使ってWebアプリを構築する必要があります。 -## [講義前の小テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17?loc=ja) +## [講義前の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ja) ## アプリの構築 @@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]])) ノートブックで作業してモデルをFlaskアプリにインポートする代わりに、Flaskアプリの中でモデルをトレーニングすることができます。おそらくデータをクリーニングした後になりますが、ノートブック内のPythonコードを変換して、アプリ内の `train` というパスでモデルを学習してみてください。この方法を採用することの長所と短所は何でしょうか？ -## [講義後の小テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18?loc=ja) +## [講義後の小テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ja) ## 振り返りと自主学習 diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md index 9110f2b8..e53b11bc 100644 --- a/3-Web-App/1-Web-App/translations/README.zh-cn.md +++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md @@ -11,7 +11,7 @@ 为此，你需要使用Flask构建一个web应用程序。 -## [课前测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/) +## [课前测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) ## 构建应用程序 @@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]])) 你可以在Flask应用程序中训练模型，而不是在notebook上工作并将模型导入Flask应用程序！尝试在notebook中转换Python代码，可能是在清除数据之后，从应用程序中的一个名为`train`的路径训练模型。采用这种方法的利弊是什么？ -## [课后测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/) +## [课后测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) ## 复习与自学 diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md index 4a337376..b6dde02f 100644 --- a/4-Classification/1-Introduction/README.md +++ b/4-Classification/1-Introduction/README.md @@ -19,7 +19,7 @@ Remember: Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) ### Introduction @@ -286,7 +286,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) ## Review & Self Study diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md index fabfec5e..76c8b0a2 100644 --- a/4-Classification/1-Introduction/translations/README.it.md +++ b/4-Classification/1-Introduction/translations/README.it.md @@ -19,7 +19,7 @@ Ricordare: La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorerà con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, è possibile determinarne la cucina di origine. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) ### Introduzione @@ -286,7 +286,7 @@ Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/refe Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) ## Revisione e Auto Apprendimento diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md index a2b1d92f..e1b32ec9 100644 --- a/4-Classification/1-Introduction/translations/README.tr.md +++ b/4-Classification/1-Introduction/translations/README.tr.md @@ -19,7 +19,7 @@ Hatırlayın: Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım. -## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/?loc=tr) +## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=tr) ### Giriş @@ -287,7 +287,7 @@ Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/ Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz? -## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/?loc=tr) +## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=tr) ## Gözden Geçirme & Kendi Kendine Çalışma diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md index df585e3f..978f4a06 100644 --- a/4-Classification/1-Introduction/translations/README.zh-cn.md +++ b/4-Classification/1-Introduction/translations/README.zh-cn.md @@ -19,7 +19,7 @@ 分类方法采用多种算法来确定其他可以用来确定一个数据点的标签或类别的方法。让我们来研究一下这个数据集，看看我们能否通过观察菜肴的原料来确定它的源头。 -## [课程前的小问题](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/) +## [课程前的小问题](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) 分类是机器学习研究者和数据科学家使用的一种基本方法。从基本的二元分类（这是不是一份垃圾邮件？）到复杂的图片分类和使用计算机视觉的分割技术，它都是将数据分类并提出相关问题的有效工具。 @@ -280,7 +280,7 @@ Scikit-learn项目提供多种对数据进行分类的算法，你需要根据 本项目的全部课程含有很多有趣的数据集。 探索一下 `data`文件夹，看看这里面有没有适合二元分类、多元分类算法的数据集，再想一下你对这些数据集有没有什么想问的问题。 -## [课后练习](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/) +## [课后练习](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) ## 回顾 & 自学 diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md index c6b35219..68877e6a 100644 --- a/4-Classification/2-Classifiers-1/README.md +++ b/4-Classification/2-Classifiers-1/README.md @@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) # Preparation Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons. @@ -67,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a Your features look like this: -| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | -| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -----: | -| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | +| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | +| ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | +| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | Now you are ready to train your model! @@ -199,8 +199,8 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use The result is printed - Indian cuisine is its best guess, with good probability: - | | 0 | - | -------: | -------: | + | | 0 | + | -------: | -------: | | indian | 0.715851 | | chinese | 0.229475 | | japanese | 0.029763 | @@ -217,21 +217,21 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use ``` | | precision | recall | f1-score | support | - | ------------ | ------ | -------- | ------- | ---- | - | chinese | 0.73 | 0.71 | 0.72 | 229 | - | indian | 0.91 | 0.93 | 0.92 | 254 | - | japanese | 0.70 | 0.75 | 0.72 | 220 | - | korean | 0.86 | 0.76 | 0.81 | 242 | - | thai | 0.79 | 0.85 | 0.82 | 254 | - | accuracy | 0.80 | 1199 | | | - | macro avg | 0.80 | 0.80 | 0.80 | 1199 | - | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | + | ------------ | --------- | ------ | -------- | ------- | + | chinese | 0.73 | 0.71 | 0.72 | 229 | + | indian | 0.91 | 0.93 | 0.92 | 254 | + | japanese | 0.70 | 0.75 | 0.72 | 220 | + | korean | 0.86 | 0.76 | 0.81 | 242 | + | thai | 0.79 | 0.85 | 0.82 | 254 | + | accuracy | 0.80 | 1199 | | | + | macro avg | 0.80 | 0.80 | 0.80 | 1199 | + | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | ## 🚀Challenge In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) ## Review & Self Study diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md index 674a59c2..5611870b 100644 --- a/4-Classification/2-Classifiers-1/translations/README.it.md +++ b/4-Classification/2-Classifiers-1/translations/README.it.md @@ -4,7 +4,7 @@ In questa lezione, si utilizzerà l'insieme di dati salvati dall'ultima lezione, Si utilizzerà questo insieme di dati con una varietà di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparerà di più su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attività di classificazione. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) # Preparazione Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni. @@ -200,13 +200,13 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_ Il risultato è stampato: la cucina indiana è la sua ipotesi migliore, con buone probabilità: - | | 0 | | | | | | | | | | | | | | | | | | | | | - | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | - | indiano | 0,715851 | | | | | | | | | | | | | | | | | | | | | - | cinese | 0.229475 | | | | | | | | | | | | | | | | | | | | | + | | 0 | | | | | | | | | | | | | | | | | | | | | + | ---------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | + | indiano | 0,715851 | | | | | | | | | | | | | | | | | | | | | + | cinese | 0.229475 | | | | | | | | | | | | | | | | | | | | | | Giapponese | 0,029763 | | | | | | | | | | | | | | | | | | | | | - | Coreano | 0.017277 | | | | | | | | | | | | | | | | | | | | | - | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | | + | Coreano | 0.017277 | | | | | | | | | | | | | | | | | | | | | + | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | | ✅ Si è in grado di spiegare perché il modello è abbastanza sicuro che questa sia una cucina indiana? @@ -217,22 +217,22 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_ print(classification_report(y_test,y_pred)) ``` - | precisione | recall | punteggio f1 | supporto | | | | | | | | | | | | | | | | | | | - | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | - | cinese | 0,73 | 0,71 | 0,72 | 229 | | | | | | | | | | | | | | | | | | - | indiano | 0,91 | 0,93 | 0,92 | 254 | | | | | | | | | | | | | | | | | | - | Giapponese | 0.70 | 0,75 | 0,72 | 220 | | | | | | | | | | | | | | | | | | - | Coreano | 0,86 | 0,76 | 0,81 | 242 | | | | | | | | | | | | | | | | | | - | thai | 0,79 | 0,85 | 0.82 | 254 | | | | | | | | | | | | | | | | | | - | accuratezza | 0,80 | 1199 | | | | | | | | | | | | | | | | | | | | - | macro media | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | | - | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | | + | precisione | recall | punteggio f1 | supporto | | | | | | | | | | | | | | | | | | | + | --------------- | ------ | ------------ | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | + | cinese | 0,73 | 0,71 | 0,72 | 229 | | | | | | | | | | | | | | | | | | + | indiano | 0,91 | 0,93 | 0,92 | 254 | | | | | | | | | | | | | | | | | | + | Giapponese | 0.70 | 0,75 | 0,72 | 220 | | | | | | | | | | | | | | | | | | + | Coreano | 0,86 | 0,76 | 0,81 | 242 | | | | | | | | | | | | | | | | | | + | thai | 0,79 | 0,85 | 0.82 | 254 | | | | | | | | | | | | | | | | | | + | accuratezza | 0,80 | 1199 | | | | | | | | | | | | | | | | | | | | + | macro media | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | | + | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 | | | | | | | | | | | | | | | | | | ## 🚀 Sfida In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) ## Revisione e Auto Apprendimento Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf) diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md index f02bd759..30f36b13 100644 --- a/4-Classification/2-Classifiers-1/translations/README.tr.md +++ b/4-Classification/2-Classifiers-1/translations/README.tr.md @@ -4,7 +4,7 @@ Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, geçen dersten kayd Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak verilen bir ulusal mutfağı öngörmek_ için kullanacaksınız. Bunu yaparken, sınıflandırma görevleri için algoritmaların leveraj edilebileceği yollardan bazıları hakkında daha fazla bilgi edineceksiniz. -## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/?loc=tr) +## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=tr) # Hazırlık [Birinci dersi](../../1-Introduction/README.md) tamamladığınızı varsayıyoruz, dolayısıyla bu dört ders için _cleaned_cuisines.csv_ dosyasının kök `/data` klasöründe var olduğundan emin olun. @@ -68,12 +68,12 @@ Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak Öznitelikleriniz şöyle görünüyor: | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | -| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | +| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | +| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | +| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | Şimdi modelinizi eğitmek için hazırsınız! @@ -199,8 +199,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine Sonuç bastırılır - Hint mutfağı iyi olasılıkla en iyi öngörü: - | | 0 | - | -------: | -------: | + | | 0 | + | -------: | -------: | | indian | 0.715851 | | chinese | 0.229475 | | japanese | 0.029763 | @@ -217,21 +217,21 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine ``` | | precision | recall | f1-score | support | - | ------------ | ------ | -------- | ------- | ---- | - | chinese | 0.73 | 0.71 | 0.72 | 229 | - | indian | 0.91 | 0.93 | 0.92 | 254 | - | japanese | 0.70 | 0.75 | 0.72 | 220 | - | korean | 0.86 | 0.76 | 0.81 | 242 | - | thai | 0.79 | 0.85 | 0.82 | 254 | - | accuracy | 0.80 | 1199 | | | - | macro avg | 0.80 | 0.80 | 0.80 | 1199 | - | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | + | ------------ | --------- | ------ | -------- | ------- | + | chinese | 0.73 | 0.71 | 0.72 | 229 | + | indian | 0.91 | 0.93 | 0.92 | 254 | + | japanese | 0.70 | 0.75 | 0.72 | 220 | + | korean | 0.86 | 0.76 | 0.81 | 242 | + | thai | 0.79 | 0.85 | 0.82 | 254 | + | accuracy | 0.80 | 1199 | | | + | macro avg | 0.80 | 0.80 | 0.80 | 1199 | + | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | ## :rocket: Meydan Okuma Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfağı öngörebilen bir makine öğrenimi modeli oluşturmak için temiz verinizi kullandınız. Scikit-learn'ün veri sınıflandırmak için sağladığı birçok yöntemi okumak için biraz vakit ayırın. Arka tarafta neler olduğunu anlamak için 'çözücü' kavramını derinlemesine inceleyin. -## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/?loc=tr) +## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=tr) ## Gözden geçirme & kendi kendine çalışma diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index 83aa4fc4..47953333 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -4,7 +4,7 @@ 你将使用此数据集和各种分类器，_根据一组配料预测这是哪一国家的美食_。在此过程中，你将学到更多用来权衡分类任务算法的方法 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) # 准备工作 假如你已经完成了[课程1](../../1-Introduction/translations/README.zh-cn.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 @@ -68,13 +68,13 @@ 你的特征集看上去将会是这样: - | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | - | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | + | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | + | ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------- | + | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 现在，你已经准备好可以开始训练你的模型了！ @@ -201,11 +201,11 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine 运行后的输出如下———可以发现这是一道印度菜的可能性最大，是最合理的猜测: | | 0 | - | -------: | -------: | + | -------: | -------: | | indian | 0.715851 | | chinese | 0.229475 | | japanese | 0.029763 | - | korean | 0.017277 | + | korean | 0.017277 | | thai | 0.007634 | ✅ 你能解释下为什么模型会如此确定这是一道印度菜么？ @@ -218,8 +218,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine ``` | precision | recall | f1-score | support | | - | ------------ | ------ | -------- | ------- | ---- | - | chinese | 0.73 | 0.71 | 0.72 | 229 | + | ------------ | ------ | -------- | ------- | ---- | + | chinese | 0.73 | 0.71 | 0.72 | 229 | | indian | 0.91 | 0.93 | 0.92 | 254 | | japanese | 0.70 | 0.75 | 0.72 | 220 | | korean | 0.86 | 0.76 | 0.81 | 242 | @@ -232,7 +232,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine 在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，这个模型能够根据输入的一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下Scikit-learn所提供的关于可以用来分类数据的其他方法的资料。此外，你也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。 -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) ## 回顾与自学 [这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背后的数学原理进行更加深入的讲解 diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md index 9720c763..291b3a75 100644 --- a/4-Classification/3-Classifiers-2/README.md +++ b/4-Classification/3-Classifiers-2/README.md @@ -2,7 +2,7 @@ In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) ### Prerequisite @@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) ## Review & Self Study diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md index 4a3a431f..3d011db8 100644 --- a/4-Classification/3-Classifiers-2/translations/README.it.md +++ b/4-Classification/3-Classifiers-2/translations/README.it.md @@ -2,7 +2,7 @@ In questa seconda lezione sulla classificazione, si esploreranno più modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) ### Prerequisito @@ -224,7 +224,7 @@ Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualità del modello. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) ## Revisione e Auto Apprendimento diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md index 24ea3dfd..56c1e11e 100644 --- a/4-Classification/3-Classifiers-2/translations/README.tr.md +++ b/4-Classification/3-Classifiers-2/translations/README.tr.md @@ -2,7 +2,7 @@ Bu ikinci sınıflandırma dersinde, sayısal veriyi sınıflandırmak için daha fazla yöntem öğreneceksiniz. Ayrıca, bir sınıflandırıcıyı diğerlerine tercih etmenin sonuçlarını da öğreneceksiniz. -## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/?loc=tr) +## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=tr) ### Ön koşul @@ -224,7 +224,7 @@ Makine Öğreniminin bu yöntemi, modelin kalitesini artırmak için, "birçok t Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi için ne anlama gelebileceği hakkında düşünün. -## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/?loc=tr) +## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr) ## Gözden Geçirme & Kendi Kendine Çalışma diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md index 66597fbf..cad728f3 100644 --- a/4-Classification/4-Applied/README.md +++ b/4-Classification/4-Applied/README.md @@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda > 🎥 Click the image above for a video: Andrew Ng introduces recommendation system design -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/) In this lesson you will learn: @@ -321,7 +321,7 @@ Congratulations, you have created a 'recommendation' web app with a few fields. Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/) ## Review & Self Study diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md index 4512aaba..72dc6c38 100644 --- a/4-Classification/4-Applied/translations/README.it.md +++ b/4-Classification/4-Applied/translations/README.it.md @@ -8,7 +8,7 @@ Uno degli usi pratici più utili dell'apprendimento automatico è la creazione d > 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng introduce la progettazione di un sistema di raccomandazione -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/) In questa lezione, si imparerà: @@ -321,7 +321,7 @@ Congratulazioni, si è creato un'app web di "raccomandazione" con pochi campi. S L'app web è molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/) ## Revisione e Auto Apprendimento diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md index 651fc85b..d3418e9a 100644 --- a/4-Classification/4-Applied/translations/README.tr.md +++ b/4-Classification/4-Applied/translations/README.tr.md @@ -8,7 +8,7 @@ Makine öğreniminin en faydalı pratik kullanımlarından biri, önerici/tavsiy > :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: Andrew Ng introduces recommendation system design (Andrew Ng önerici sistem tasarımını tanıtıyor) -## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/?loc=tr) +## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=tr) Bu derste şunları öğreneceksiniz: @@ -321,7 +321,7 @@ Tebrikler, birkaç değişkenle bir 'önerici' web uygulaması oluşturdunuz! Bu Web uygulamanız çok minimal, bu yüzden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulamanızı oluşturmaya devam edin. Verilen bir ulusal yemeği yapmak için hangi tat birleşimleri işe yarıyor? -## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/?loc=tr) +## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr) ## Gözden Geçirme & Kendi Kendine Çalışma diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index 8453c451..c5ef453f 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -5,7 +5,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare") > 🎥 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) ### Introduction [Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music. @@ -317,7 +317,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) ## Review & Self Study diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md index 104507c9..0d8510b0 100644 --- a/5-Clustering/1-Visualize/translations/README.it.md +++ b/5-Clustering/1-Visualize/translations/README.it.md @@ -5,7 +5,7 @@ Il clustering è un tipo di [apprendimento non supervisionato](https://wikipedia [![No One Like You di PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You di PSquare") > 🎥 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa è una canzone molto apprezzata del 2014 di PSquare. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) ### Introduzione @@ -33,18 +33,18 @@ In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/tra [Scikit-learn offre una vasta gamma](https://scikit-learn.org/stable/modules/clustering.html) di metodi per eseguire il clustering. Il tipo scelto dipenderà dal caso d'uso. Secondo la documentazione, ogni metodo ha diversi vantaggi. Ecco una tabella semplificata dei metodi supportati da Scikit-learn e dei loro casi d'uso appropriati: -| Nome del metodo | Caso d'uso | -| :--------------------------- | :--------------------------------------------------------------------- | -| K-MEANS | uso generale, induttivo | -| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo | -| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo | -| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi | -| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi | -| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive | -| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo | -| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo | -| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva | -| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo | +| Nome del metodo | Caso d'uso | +| :------------------------------------------------------ | :-------------------------------------------------------------------------- | +| K-MEANS | uso generale, induttivo | +| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo | +| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo | +| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi | +| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi | +| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive | +| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo | +| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo | +| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva | +| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo | > 🎓 Il modo in cui si creno i cluster ha molto a che fare con il modo in cui si raccolgono punti dati in gruppi. Si esamina un po' di vocabolario: > @@ -197,16 +197,16 @@ Il clustering come tecnica è notevolmente aiutato da una corretta visualizzazio df.describe() ``` - | | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma | - | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- | - | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | - | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 | - | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 | - | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 | - | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 | - | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 | - | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 | - | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 | + | | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma | + | ------- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ----------- | -------- | ---------------------------- | -------- | ---------- | ---------- | + | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | + | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 | + | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 | + | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 | + | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 | + | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 | + | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 | + | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 | > 🤔 Se si sta lavorando con il clustering, un metodo non supervisionato che non richiede dati etichettati, perché si stanno mostrando questi dati con etichette? Nella fase di esplorazione dei dati, sono utili, ma non sono necessari per il funzionamento degli algoritmi di clustering. Si potrebbero anche rimuovere le intestazioni delle colonne e fare riferimento ai dati per numero di colonna. @@ -319,7 +319,7 @@ In generale, per il clustering è possibile utilizzare i grafici a dispersione p In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) ## Revisione e Auto Apprendimento diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md index 5f41ffa7..e3161473 100644 --- a/5-Clustering/1-Visualize/translations/README.zh-cn.md +++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md @@ -4,7 +4,7 @@ [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare") > 🎥 点击上面的图片观看视频。当您通过聚类学习机器学习时，请欣赏一些尼日利亚舞厅曲目 - 这是2014 年PSquare上高度评价的歌曲。 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) ### 介绍 [聚类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数据探索非常有用。让我们看看它是否有助于发现尼日利亚观众消费音乐的趋势和模式。 @@ -323,7 +323,7 @@ 聚类试图解决什么样的问题？ -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) ## 复习与自学 diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md index d85654ae..3f4c5fca 100644 --- a/5-Clustering/2-K-Means/README.md +++ b/5-Clustering/2-K-Means/README.md @@ -4,7 +4,7 @@ > 🎥 Click the image above for a video: Andrew Ng explains clustering -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started! @@ -238,7 +238,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226). -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) ## Review & Self Study diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md index ce1dc9d2..9c94d19f 100644 --- a/5-Clustering/2-K-Means/translations/README.it.md +++ b/5-Clustering/2-K-Means/translations/README.it.md @@ -4,7 +4,7 @@ > 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) In questa lezione si imparerà come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si proverà K-Means poiché è la tecnica di clustering più comune. Si inizia! @@ -238,7 +238,7 @@ Trascorrere un po' di tempo con questo notebook, modificando i parametri. E poss Suggerimento: provare a ridimensionare i dati. C'è un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati più simili tra loro in termini di intervallo. Si scoprirà che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perché lasciare i dati non scalati consente ai dati con meno varianza di avere più peso. Leggere un po' di più su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226). -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) ## Revisione e Auto Apprendimento diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md index 32a33015..a5348eac 100644 --- a/5-Clustering/2-K-Means/translations/README.zh-cn.md +++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md @@ -4,7 +4,7 @@ > 🎥 单击上图观看视频：Andrew Ng 解释聚类 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) 在本课中，您将学习如何使用 Scikit-learn 和您之前导入的尼日利亚音乐数据集创建聚类。我们将介绍 K-Means 聚类 的基础知识。请记住，正如您在上一课中学到的，使用聚类的方法有很多种，您使用的方法取决于您的数据。我们将尝试 K-Means，因为它是最常见的聚类技术。让我们开始吧！ @@ -239,7 +239,7 @@ K-Means 聚类过程[分三步执行](https://scikit-learn.org/stable/modules/cl 提示：尝试缩放您的数据。笔记本中的注释代码添加了标准缩放，使数据列在范围方面更加相似。您会发现，当轮廓分数下降时，肘部图中的“扭结”变得平滑。这是因为不缩放数据可以让方差较小的数据承载更多的权重。在[这里](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)阅读更多关于这个问题的[信息](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)。 -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) ## 复习与自学 diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index 51235856..ef7444cb 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -2,7 +2,7 @@ This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) ## Introduction @@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) ## Review & Self Study diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md index e85bd136..67c0406e 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.it.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md @@ -2,7 +2,7 @@ Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) ## Introduzione @@ -149,7 +149,7 @@ Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a imple Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) ## Revisione e Auto Apprendimento diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md index e9df88a3..3826ccfd 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md @@ -1,7 +1,7 @@ # 自然语言处理介绍 这节课讲解了 *自然语言处理* 的简要历史和重要概念，*自然语言处理*是计算语言学的一个子领域。 -## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/) +## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) ## 介绍 众所周知，自然语言处理 (Natural Language Processing, NLP) 是机器学习在生产软件中应用最广泛的领域之一。 @@ -146,7 +146,7 @@ 在下一课中，您将了解解析自然语言和机器学习的许多其他方法。 -## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/) +## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) ## 复习与自学 diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md index d816b7fe..829df67b 100644 --- a/6-NLP/2-Tasks/README.md +++ b/6-NLP/2-Tasks/README.md @@ -2,7 +2,7 @@ For most *natural language processing* tasks, the text to be processed, must be broken down, examined, and the results stored or cross referenced with rules and data sets. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/) Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist. @@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?' -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/) ## Review & Self Study diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md index afe455e7..d553eea3 100644 --- a/6-NLP/2-Tasks/translations/README.it.md +++ b/6-NLP/2-Tasks/translations/README.it.md @@ -2,7 +2,7 @@ Per la maggior parte delle attività di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attività consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/) Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantità di testo in modo efficiente. Prima di applicare machine learning a queste attività, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP. @@ -203,7 +203,7 @@ Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un a Prendere un'attività dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?' -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/) ## Revisione e Auto Apprendimento diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index 0c6b568b..dd6bd1bf 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -2,7 +2,7 @@ In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/) Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps: @@ -176,7 +176,7 @@ Here is a sample [solution](solution/notebook.ipynb). Can you make Marvin even better by extracting other features from the user input? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/) ## Review & Self Study diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md index 32e52922..d1116a9e 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.it.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md @@ -2,7 +2,7 @@ Nelle lezioni precedenti si è imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attività di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale è _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/) La traduzione è un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna può avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi: @@ -176,7 +176,7 @@ Ecco una [soluzione](../solution/notebook.ipynb) di esempio. Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input dell'utente? -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/) ## Revisione e Auto Apprendimento diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index a4b5a556..dd6a573a 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e - how to calculate some new data based on the existing columns - how to save the resulting dataset for use in the final challenge -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/) ### Introduction @@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/) ## Review & Self Study diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md index a541d0d6..cbbca610 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md @@ -6,7 +6,7 @@ In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseg - come calcolare alcuni nuovi dati in base alle colonne esistenti - come salvare l'insieme di dati risultante per l'uso nella sfida finale -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/) ### Introduzione @@ -287,15 +287,15 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s display(hotel_freq_df) ``` - | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found | - | :----------------------------------------: | :---------------------: | :-----------------: | - | Britannia International Hotel Canary Wharf | 9086 | 4789 | - | Park Plaza Westminster Bridge Londra | 12158 | 4169 | - | Copthorne Tara Hotel London Kensington | 7105 | 3578 | - | ... | ... | ... | - | Mercure Paris Porte d'Orléans | 110 | 10 | - | Hotel Wagner | 135 | 10 | - | Hotel Gallitzinberg | 173 | 8 | + | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found | + | :----------------------------------------: | :---------------------------------------------------: | :-----------------: | + | Britannia International Hotel Canary Wharf | 9086 | 4789 | + | Park Plaza Westminster Bridge Londra | 12158 | 4169 | + | Copthorne Tara Hotel London Kensington | 7105 | 3578 | + | ... | ... | ... | + | Mercure Paris Porte d'Orléans | 110 | 10 | + | Hotel Wagner | 135 | 10 | + | Hotel Gallitzinberg | 173 | 8 | Si potrebbe notare che il *conteggio nell'insieme di dati* non corrisponde al valore in `Total_Number_of_Reviews`. Non è chiaro se questo valore nell'insieme di dati rappresentasse il numero totale di recensioni che l'hotel aveva, ma non tutte sono state recuperate o qualche altro calcolo. `Total_Number_of_Reviews` non viene utilizzato nel modello a causa di questa non chiarezza. @@ -324,19 +324,19 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s Ci si potrebbe anche chiedere del valore `Average_Score` e perché a volte è diverso dal punteggio medio calcolato. Poiché non è possibile sapere perché alcuni valori corrispondano, ma altri hanno una differenza, in questo caso è più sicuro utilizzare i punteggi delle recensioni a disposizione per calcolare autonomamente la media. Detto questo, le differenze sono solitamente molto piccole, ecco gli hotel con la maggiore deviazione dalla media dell'insieme di dati e dalla media calcolata: - | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) | - | :----------------------: | :-----------: | :----------------: | ------------------------------------------: | - | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria | - | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery | - | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans | - | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel | - | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es | - | ... | ... | ... | ... | - | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre | - | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur | - | 0,9 | 6.8 | 5,9 | Villa Eugenia | - | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux | - | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar | + | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) | + | :----------------------: | :-----------: | :----------------: | -------------------------------------------: | + | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria | + | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery | + | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans | + | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel | + | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es | + | ... | ... | ... | ... | + | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre | + | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur | + | 0,9 | 6.8 | 5,9 | Villa Eugenia | + | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux | + | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar | Con un solo hotel con una differenza di punteggio maggiore di 1, significa che probabilmente si può ignorare la differenza e utilizzare il punteggio medio calcolato. @@ -401,7 +401,7 @@ Ora che si è esplorato l'insieme di dati, nella prossima lezione si filtreranno Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/) ## Revisione e Auto Apprendimento diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index 12b9a15a..c764b8e1 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,7 +1,7 @@ # Sentiment analysis with hotel reviews Now that you have a explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/) ### Filtering & Sentiment Analysis Operations @@ -360,7 +360,7 @@ To review, the steps are: When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/) ## Challenge diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md index f6d22b56..46e92c7b 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md @@ -2,7 +2,7 @@ Ora che si è esplorato in dettaglio l'insieme di dati, è il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel. -## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/) +## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/) ### Operazioni di Filtraggio e Analisi del Sentiment @@ -59,14 +59,14 @@ Occorre pulire un po' di più i dati. Si aggiungono colonne che saranno utili in display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"})) ``` - | Hotel_Address | Hotel_Name (Nome Hotel) | - | :--------------------- | :--------: | - | Amsterdam, Paesi Bassi | 105 | - | Barcellona, Spagna | 211 | - | Londra, Regno Unito | 400 | - | Milano, Italia | 162 | - | Parigi, Francia | 458 | - | Vienna, Austria | 158 | + | Hotel_Address | Hotel_Name (Nome Hotel) | + | :--------------------- | :---------------------: | + | Amsterdam, Paesi Bassi | 105 | + | Barcellona, Spagna | 211 | + | Londra, Regno Unito | 400 | + | Milano, Italia | 162 | + | Parigi, Francia | 458 | + | Vienna, Austria | 158 | 2. Elaborazione colonne di meta-recensione dell'hotel @@ -361,7 +361,7 @@ Per riepilogare, i passaggi sono: Quando si è iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ciò che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale. -## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/) +## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/) ## Sfida diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md index ae2a69e1..1d75763f 100644 --- a/7-TimeSeries/1-Introduction/README.md +++ b/7-TimeSeries/1-Introduction/README.md @@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for > 🎥 Click the image above for a video about time series forecasting -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/) It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques. @@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts. Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else? -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/) ## Review & Self Study diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index 3134d939..19da5622 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load > 🎥 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/) ## Introduction @@ -383,7 +383,7 @@ Check the accuracy of your model by testing its mean absolute percentage error ( Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html) -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/) ## Review & Self Study diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index 6301c46e..fbba8352 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to > 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/45/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/) ## Prerequisites and Setup @@ -314,6 +314,6 @@ The learnings can be summarized as: Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/46/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/) ## Assignment [A More Realistic World](assignment.md) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index b1a2e2a2..933967fe 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -2,7 +2,7 @@ The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/) ## Introduction @@ -329,7 +329,7 @@ You should see something like this: > **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/) ## Assignment: [Train a Mountain Car](assignment.md) diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index 154c57d6..b06b8b71 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/49/) +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/) ## 💰 Finance @@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/ Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML. -## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/50/) +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/) ## Review & Self Study diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md index 22b0d37a..01f2f139 100644 --- a/TRANSLATIONS.md +++ b/TRANSLATIONS.md @@ -26,7 +26,7 @@ Similar to Readme's, please translate the assignments as well. 3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/ML-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language. -4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1 becomes https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=id +4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=id **THANK YOU**