From f9e549888ac2f342da1f2af322b8487df88dce2c Mon Sep 17 00:00:00 2001 From: Julia Muiruri Date: Thu, 14 Jul 2022 21:05:56 +0300 Subject: [PATCH] updated all quiz links --- 1-Introduction/1-intro-to-ML/README.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.bn.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.es.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.id.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.it.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.ja.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.ko.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.pt-br.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.ru.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.tr.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.zh-cn.md | 4 ++-- 1-Introduction/1-intro-to-ML/translations/README.zh-tw.md | 4 ++-- 1-Introduction/2-history-of-ML/README.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.es.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.fr.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.id.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.it.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.ja.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.ko.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.pt-br.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.ru.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.tr.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.zh-cn.md | 4 ++-- 1-Introduction/2-history-of-ML/translations/README.zh-tw.md | 4 ++-- 1-Introduction/3-fairness/README.md | 4 ++-- 1-Introduction/3-fairness/translations/README.es.md | 4 ++-- 1-Introduction/3-fairness/translations/README.fr.md | 4 ++-- 1-Introduction/3-fairness/translations/README.id.md | 4 ++-- 1-Introduction/3-fairness/translations/README.it.md | 4 ++-- 1-Introduction/3-fairness/translations/README.ja.md | 4 ++-- 1-Introduction/3-fairness/translations/README.ko.md | 4 ++-- 1-Introduction/3-fairness/translations/README.pt-br.md | 4 ++-- 1-Introduction/3-fairness/translations/README.zh-cn.md | 4 ++-- 1-Introduction/3-fairness/translations/README.zh-tw.md | 4 ++-- 1-Introduction/4-techniques-of-ML/README.md | 4 ++-- 1-Introduction/4-techniques-of-ML/translations/README.es.md | 4 ++-- 1-Introduction/4-techniques-of-ML/translations/README.id.md | 4 ++-- 1-Introduction/4-techniques-of-ML/translations/README.it.md | 4 ++-- 1-Introduction/4-techniques-of-ML/translations/README.ja.md | 4 ++-- 1-Introduction/4-techniques-of-ML/translations/README.ko.md | 4 ++-- .../4-techniques-of-ML/translations/README.pt-br.md | 4 ++-- .../4-techniques-of-ML/translations/README.zh-cn.md | 4 ++-- .../4-techniques-of-ML/translations/README.zh-tw.md | 4 ++-- 2-Regression/1-Tools/README.md | 4 ++-- 2-Regression/1-Tools/translations/README.id.md | 4 ++-- 2-Regression/1-Tools/translations/README.it.md | 4 ++-- 2-Regression/1-Tools/translations/README.ja.md | 4 ++-- 2-Regression/1-Tools/translations/README.ko.md | 4 ++-- 2-Regression/1-Tools/translations/README.pt-br.md | 4 ++-- 2-Regression/1-Tools/translations/README.pt.md | 4 ++-- 2-Regression/1-Tools/translations/README.tr.md | 4 ++-- 2-Regression/1-Tools/translations/README.zh-cn.md | 4 ++-- 2-Regression/1-Tools/translations/README.zh-tw.md | 4 ++-- 2-Regression/2-Data/README.md | 4 ++-- 2-Regression/2-Data/translations/README.es.md | 4 ++-- 2-Regression/2-Data/translations/README.id.md | 4 ++-- 2-Regression/2-Data/translations/README.it.md | 4 ++-- 2-Regression/2-Data/translations/README.ja.md | 4 ++-- 2-Regression/2-Data/translations/README.ko.md | 4 ++-- 2-Regression/2-Data/translations/README.pt-br.md | 4 ++-- 2-Regression/2-Data/translations/README.pt.md | 4 ++-- 2-Regression/2-Data/translations/README.zh-cn.md | 4 ++-- 2-Regression/2-Data/translations/README.zh-tw.md | 4 ++-- 2-Regression/3-Linear/README.md | 4 ++-- 2-Regression/3-Linear/solution/R/lesson_3-R.ipynb | 2 +- 2-Regression/3-Linear/solution/R/lesson_3.Rmd | 2 +- 2-Regression/3-Linear/translations/README.es.md | 4 ++-- 2-Regression/3-Linear/translations/README.id.md | 4 ++-- 2-Regression/3-Linear/translations/README.it.md | 4 ++-- 2-Regression/3-Linear/translations/README.ja.md | 4 ++-- 2-Regression/3-Linear/translations/README.ko.md | 4 ++-- 2-Regression/3-Linear/translations/README.pt-br.md | 4 ++-- 2-Regression/3-Linear/translations/README.pt.md | 4 ++-- 2-Regression/3-Linear/translations/README.zh-cn.md | 4 ++-- 2-Regression/3-Linear/translations/README.zh-tw.md | 4 ++-- 2-Regression/4-Logistic/README.md | 4 ++-- 2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb | 2 +- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 2 +- 2-Regression/4-Logistic/translations/README.es.md | 4 ++-- 2-Regression/4-Logistic/translations/README.id.md | 4 ++-- 2-Regression/4-Logistic/translations/README.it.md | 4 ++-- 2-Regression/4-Logistic/translations/README.ja.md | 4 ++-- 2-Regression/4-Logistic/translations/README.ko.md | 4 ++-- 2-Regression/4-Logistic/translations/README.pt-br.md | 4 ++-- 2-Regression/4-Logistic/translations/README.pt.md | 4 ++-- 2-Regression/4-Logistic/translations/README.zh-cn.md | 4 ++-- 2-Regression/4-Logistic/translations/README.zh-tw.md | 4 ++-- 3-Web-App/1-Web-App/README.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.es.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.it.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.ja.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.ko.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.pt-br.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.pt.md | 4 ++-- 3-Web-App/1-Web-App/translations/README.zh-cn.md | 4 ++-- 4-Classification/1-Introduction/README.md | 4 ++-- 4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb | 4 ++-- 4-Classification/1-Introduction/solution/R/lesson_10.Rmd | 4 ++-- 4-Classification/1-Introduction/translations/README.es.md | 4 ++-- 4-Classification/1-Introduction/translations/README.it.md | 4 ++-- 4-Classification/1-Introduction/translations/README.ko.md | 4 ++-- 4-Classification/1-Introduction/translations/README.pt-br.md | 4 ++-- 4-Classification/1-Introduction/translations/README.tr.md | 4 ++-- 4-Classification/1-Introduction/translations/README.zh-cn.md | 4 ++-- 4-Classification/2-Classifiers-1/README.md | 4 ++-- 4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb | 2 +- 4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd | 2 +- 4-Classification/2-Classifiers-1/translations/README.es.md | 4 ++-- 4-Classification/2-Classifiers-1/translations/README.it.md | 4 ++-- 4-Classification/2-Classifiers-1/translations/README.ko.md | 4 ++-- 4-Classification/2-Classifiers-1/translations/README.pt-br.md | 4 ++-- 4-Classification/2-Classifiers-1/translations/README.tr.md | 4 ++-- 4-Classification/2-Classifiers-1/translations/README.zh-cn.md | 4 ++-- 4-Classification/3-Classifiers-2/README.md | 4 ++-- 4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb | 4 ++-- 4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.es.md | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.it.md | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.ko.md | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.pt-br.md | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.tr.md | 4 ++-- 4-Classification/3-Classifiers-2/translations/README.zh-cn.md | 4 ++-- 4-Classification/4-Applied/README.md | 4 ++-- 4-Classification/4-Applied/translations/README.es.md | 4 ++-- 4-Classification/4-Applied/translations/README.it.md | 4 ++-- 4-Classification/4-Applied/translations/README.ko.md | 4 ++-- 4-Classification/4-Applied/translations/README.pt-br.md | 4 ++-- 4-Classification/4-Applied/translations/README.tr.md | 4 ++-- 4-Classification/4-Applied/translations/README.zh-CN.md | 4 ++-- 5-Clustering/1-Visualize/README.md | 4 ++-- 5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb | 4 ++-- 5-Clustering/1-Visualize/solution/R/lesson_14.Rmd | 4 ++-- 5-Clustering/1-Visualize/translations/README.es.md | 4 ++-- 5-Clustering/1-Visualize/translations/README.it.md | 4 ++-- 5-Clustering/1-Visualize/translations/README.ko.md | 4 ++-- 5-Clustering/1-Visualize/translations/README.zh-cn.md | 4 ++-- 5-Clustering/2-K-Means/README.md | 4 ++-- 5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb | 4 ++-- 5-Clustering/2-K-Means/solution/R/lesson_15.Rmd | 4 ++-- 5-Clustering/2-K-Means/translations/README.es.md | 4 ++-- 5-Clustering/2-K-Means/translations/README.it.md | 4 ++-- 5-Clustering/2-K-Means/translations/README.ko.md | 4 ++-- 5-Clustering/2-K-Means/translations/README.zh-cn.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/README.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/translations/README.es.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/translations/README.it.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/translations/README.ko.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md | 4 ++-- 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 4 ++-- 6-NLP/2-Tasks/README.md | 4 ++-- 6-NLP/2-Tasks/translations/README.es.md | 4 ++-- 6-NLP/2-Tasks/translations/README.it.md | 4 ++-- 6-NLP/2-Tasks/translations/README.ko.md | 4 ++-- 6-NLP/2-Tasks/translations/README.pt-br.md | 4 ++-- 6-NLP/3-Translation-Sentiment/README.md | 4 ++-- 6-NLP/3-Translation-Sentiment/translations/README.es.md | 4 ++-- 6-NLP/3-Translation-Sentiment/translations/README.it.md | 4 ++-- 6-NLP/3-Translation-Sentiment/translations/README.ko.md | 4 ++-- 6-NLP/4-Hotel-Reviews-1/README.md | 4 ++-- 6-NLP/4-Hotel-Reviews-1/translations/README.es.md | 4 ++-- 6-NLP/4-Hotel-Reviews-1/translations/README.it.md | 4 ++-- 6-NLP/4-Hotel-Reviews-1/translations/README.ko.md | 4 ++-- 6-NLP/5-Hotel-Reviews-2/README.md | 4 ++-- 6-NLP/5-Hotel-Reviews-2/translations/README.es.md | 4 ++-- 6-NLP/5-Hotel-Reviews-2/translations/README.it.md | 4 ++-- 6-NLP/5-Hotel-Reviews-2/translations/README.ko.md | 4 ++-- 7-TimeSeries/1-Introduction/README.md | 4 ++-- 7-TimeSeries/1-Introduction/translations/README.es.md | 4 ++-- 7-TimeSeries/1-Introduction/translations/README.it.md | 4 ++-- 7-TimeSeries/1-Introduction/translations/README.ko.md | 4 ++-- 7-TimeSeries/2-ARIMA/README.md | 4 ++-- 7-TimeSeries/2-ARIMA/translations/README.it.md | 4 ++-- 7-TimeSeries/2-ARIMA/translations/README.ko.md | 4 ++-- 7-TimeSeries/3-SVR/README.md | 4 ++-- 8-Reinforcement/1-QLearning/README.md | 4 ++-- 8-Reinforcement/1-QLearning/translations/README.it.md | 4 ++-- 8-Reinforcement/1-QLearning/translations/README.ko.md | 4 ++-- 8-Reinforcement/1-QLearning/translations/README.zh-cn.md | 4 ++-- 8-Reinforcement/2-Gym/README.md | 4 ++-- 8-Reinforcement/2-Gym/translations/README.it.md | 4 ++-- 8-Reinforcement/2-Gym/translations/README.ko.md | 4 ++-- 8-Reinforcement/2-Gym/translations/README.zh-cn.md | 4 ++-- 9-Real-World/1-Applications/README.md | 4 ++-- 9-Real-World/1-Applications/translations/README.it.md | 4 ++-- 9-Real-World/1-Applications/translations/README.ko.md | 4 ++-- TRANSLATIONS.md | 2 +- 187 files changed, 367 insertions(+), 367 deletions(-) diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index 715843ff..36ee822c 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -8,7 +8,7 @@ Watch the video, then take the pre-lesson quiz > 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning. -## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/) --- @@ -134,7 +134,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +# [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) --- # Review & Self Study diff --git a/1-Introduction/1-intro-to-ML/translations/README.bn.md b/1-Introduction/1-intro-to-ML/translations/README.bn.md index 97e6b1e8..3eaed0fe 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.bn.md +++ b/1-Introduction/1-intro-to-ML/translations/README.bn.md @@ -7,7 +7,7 @@ Watch the video, then take the pre-lesson quiz > 🎥 মেশিন লার্নিং, এআই(আর্টিফিশিয়াল ইন্টিলিজেন্স) এবং ডিপ লার্নিং এর মধ্যে পার্থক্য এর আলোচনা জানতে উপরের ছবিটিতে ক্লিক করে ভিডিওটি দেখুন। -## [প্রি-লেকচার-কুইজ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [প্রি-লেকচার-কুইজ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/) --- বিগিনারদের জন্য ক্লাসিক্যাল মেশিন লার্নিং কোর্স এ আপনাকে স্বাগতম!আপনি হয় এই বিষয়ে সম্পূর্ণ নতুন অথবা মেশিন লার্নিং এ নিজের অনুশীলনকে আরও উন্নত করতে চান, আপনি আমাদের সাথে যোগদান করতে পেরে আমরা খুশি! আমরা আপনার ML অধ্যয়নের জন্য একটি বন্ধুত্বপূর্ণ লঞ্চিং স্পট তৈরি করতে চাই এবং আপনার মূল্যায়ন, প্রতিক্রিয়া,[ফিডব্যাক](https://github.com/microsoft/ML-For-Beginners/discussions). জানাতে এবং অন্তর্ভুক্ত করতে পেরে খুশি হব । @@ -136,7 +136,7 @@ MIT এর জন গাটেং মেশিন লার্নিং এর স্কেচ, কাগজে বা একটি অনলাইন অ্যাপ ব্যবহার করে [এক্সালিড্র](https://excalidraw.com/) AI, ML, ডিপ লার্নিং এবং ডেটা সায়েন্সের মধ্যে পার্থক্য সম্পর্কে। -# [লেকচার-কুইজ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +# [লেকচার-কুইজ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) --- # পর্যালোচনা ও সেল্ফ স্টাডি diff --git a/1-Introduction/1-intro-to-ML/translations/README.es.md b/1-Introduction/1-intro-to-ML/translations/README.es.md index 28cfa2e9..fd193a0d 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.es.md +++ b/1-Introduction/1-intro-to-ML/translations/README.es.md @@ -4,7 +4,7 @@ > 🎥 Haz clic en la imagen de arriba para ver un video donde se discuten las diferencias entre el machine learning, la inteligencia artificial, y el deep learning. -## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=es) +## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=es) ### Introducción @@ -100,7 +100,7 @@ En el futuro próximo, entender las bases de machine learning va a ser una neces Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.com/), cómo entiendes las diferencias entre inteligencia artificial, ML, deep learning, y la ciencia de datos. Agrega algunas ideas de problemas que cada una de estas técnicas son buenas en resolver. -## [Cuestionario después de la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=es) +## [Cuestionario después de la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=es) ## Revisión y autoestudio 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 90798160..717fedf9 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=fr) +## [Quiz préalable](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=fr) +## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.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 69a9157b..623e9fd1 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.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 eab0f490..b7806dcb 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/?loc=it) ### 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://white-water-09ec41f0f.azurestaticapps.net/quiz/2/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/?loc=it) ## 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 b88738d0..563abdea 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ja) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ja) ## 振り返りと自習 diff --git a/1-Introduction/1-intro-to-ML/translations/README.ko.md b/1-Introduction/1-intro-to-ML/translations/README.ko.md index e19a23a8..b62f1577 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ko.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ko.md @@ -4,7 +4,7 @@ > 🎥 머신러닝, AI 그리고 딥러닝의 차이를 설명하는 영상을 보려면 위 이미지를 클릭합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/) ### 소개 @@ -100,7 +100,7 @@ 종이에 그리거나, [Excalidraw](https://excalidraw.com/)처럼 온라인 앱을 이용하여 AI, ML, 딥러닝, 그리고 데이터 사이언스의 차이를 이해합시다. 각 기술들이 잘 해결할 수 있는 문제에 대해 아이디어를 합쳐보세요. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) ## 리뷰 & 자기주도 학습 diff --git a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md index 644a2bb0..1f0d51ef 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md +++ b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md @@ -4,7 +4,7 @@ > 🎥 Clique na imagem acima para assistir um vídeo que ilustra a diferença entre machine learning, AI, e deep learning. -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=ptbr) ### Introdução @@ -100,7 +100,7 @@ Em um futuro próximo, compreender os fundamentos do machine learning será uma Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excalidraw.com/), sua compreensão das diferenças entre AI, ML, deep learning e data science. Adicione algumas idéias de problemas que cada uma dessas técnicas é boa para resolver. -## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ptbr) +## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2?loc=ptbr) ## Revisão e autoestudo diff --git a/1-Introduction/1-intro-to-ML/translations/README.ru.md b/1-Introduction/1-intro-to-ML/translations/README.ru.md index eeebc7df..c61b5a52 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ru.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ru.md @@ -8,7 +8,7 @@ > 🎥 Нажмите на изображение выше, чтобы просмотреть видео, в котором обсуждается разница между машинным обучением, искусственным интеллектом и глубоким обучением. -## [Тест перед лекцией](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [Тест перед лекцией](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/) --- @@ -134,7 +134,7 @@ Набросайте на бумаге или с помощью онлайн-приложения, такого как [Excalidraw](https://excalidraw.com/), ваше понимание различий между AI, ML, глубоким обучением и наукой о данных. Добавьте несколько идей о проблемах, которые может решить каждый из этих методов. -# [Тест после лекции](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +# [Тест после лекции](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) --- # Обзор и самообучение 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 669e649d..82dd91e2 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=tr) +## [Ders öncesi sınav](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=tr) +## [Ders sonrası test](https://gentle-hill-034defd0f.1.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 a2959603..22133bd4 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [课前测验](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +## [阅读后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) ## 复习与自学 diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md index 0d08153f..9e55679b 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md +++ b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md @@ -3,7 +3,7 @@ [![機器學習，人工智能，深度學習-有什麽區別?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "機器學習，人工智能，深度學習-有什麽區別?") > 🎥 點擊上面的圖片觀看討論機器學習、人工智能和深度學習之間區別的視頻。 -## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/) +## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1/) ### 介紹 @@ -92,7 +92,7 @@ 在紙上或使用 [Excalidraw](https://excalidraw.com/) 等在線應用程序繪製草圖，了解你對 AI、ML、深度學習和數據科學之間差異的理解。添加一些關於這些技術擅長解決的問題的想法。 -## [閱讀後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/) +## [閱讀後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/2/) ## 復習與自學 diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md index 53ca9ee7..a26fce77 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/) --- @@ -128,7 +128,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.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 7ef7d80a..b878e4e1 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 historia 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=es) +## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=es) 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 ocurrió ningún descubrimiento científico en un vacío cultural. ¿Qué descubres? -## [Cuestionario posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=es) +## [Cuestionario posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=es) ## 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 efe26877..9c6c6687 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=fr) +## [Quizz préalable](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=fr) +## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.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 9e695a8a..47ce2816 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.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 e6fc2d90..f4f678aa 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/?loc=it) 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://white-water-09ec41f0f.azurestaticapps.net/quiz/4/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/?loc=it) ## 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 6ba32096..c3eeedb2 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ja) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ja) ## 振り返りと自習 diff --git a/1-Introduction/2-history-of-ML/translations/README.ko.md b/1-Introduction/2-history-of-ML/translations/README.ko.md index d630201e..3c61b5e7 100644 --- a/1-Introduction/2-history-of-ML/translations/README.ko.md +++ b/1-Introduction/2-history-of-ML/translations/README.ko.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) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/) 이 강의에서, 머신러닝과 인공 지능의 역사에서 주요 마일스톤을 살펴보려 합니다. @@ -103,7 +103,7 @@ natural language processing 연구가 발전하고, 검색이 개선되어 더 역사적인 순간에 사람들 뒤에서 한 가지를 집중적으로 파고 있는 자를 자세히 알아보세요. 매력있는 캐릭터가 있으며, 문화가 사라진 곳에서는 과학적인 발견을 하지 못합니다. 당신은 어떤 발견을 해보았나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/) ## 검토 & 자기주도 학습 diff --git a/1-Introduction/2-history-of-ML/translations/README.pt-br.md b/1-Introduction/2-history-of-ML/translations/README.pt-br.md index 57ae435d..d9fa1471 100644 --- a/1-Introduction/2-history-of-ML/translations/README.pt-br.md +++ b/1-Introduction/2-history-of-ML/translations/README.pt-br.md @@ -3,7 +3,7 @@ ![Resumo da história do machine learning no sketchnote](../../../sketchnotes/ml-history.png) > Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ptbr) +## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3?loc=ptbr) Nesta lição, veremos os principais marcos da história do machine learning e da artificial intelligence. @@ -103,7 +103,7 @@ Resta saber o que o futuro reserva, mas é importante entender esses sistemas de Explore um desses momentos históricos e aprenda mais sobre as pessoas por trás deles. Existem personagens fascinantes e nenhuma descoberta científica foi criada em um vácuo cultural. O que você descobriu? -## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ptbr) +## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4?loc=ptbr) ## Revisão e Autoestudo diff --git a/1-Introduction/2-history-of-ML/translations/README.ru.md b/1-Introduction/2-history-of-ML/translations/README.ru.md index 65c72724..5dbfa2cb 100644 --- a/1-Introduction/2-history-of-ML/translations/README.ru.md +++ b/1-Introduction/2-history-of-ML/translations/README.ru.md @@ -3,7 +3,7 @@ ![Краткое изложение истории машинного обучения в заметке](../../../sketchnotes/ml-history.png) > Заметка [Томоми Имура](https://www.twitter.com/girlie_mac) -## [Тест перед лекцией](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [Тест перед лекцией](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/) --- @@ -128,7 +128,7 @@ Погрузитесь в один из этих исторических моментов и узнайте больше о людях, стоящих за ними. Есть увлекательные персонажи, и ни одно научное открытие никогда не создавалось в культурном вакууме. Что вы обнаружите? -## [Тест после лекции](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [Тест после лекции](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/) --- ## Обзор и самообучение 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 af2346fb..d9277d40 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=tr) +## [Ders öncesi test](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=tr) +## [Ders sonrası test](https://gentle-hill-034defd0f.1.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 700c1320..e1184b6d 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://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [课前测验](https://gentle-hill-034defd0f.1.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](htt 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://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/) ## 复习与自学 diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md index 58915f85..4fb491d2 100644 --- a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md +++ b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md @@ -2,7 +2,7 @@ ![機器學習歷史概述](../../../sketchnotes/ml-history.png) > 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/) +## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/3/) 在本課中，我們將走過機器學習和人工智能歷史上的主要裏程碑。 @@ -95,7 +95,7 @@ Alan Turing，一個真正傑出的人，[在 2019 年被公眾投票選出](htt 深入了解這些歷史時刻之一，並更多地了解它們背後的人。這裏有許多引人入勝的人物，沒有一項科學發現是在文化真空中創造出來的。你發現了什麽？ -## [課後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/) +## [課後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/4/) ## 復習與自學 diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index d4ebfdf2..baa27562 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://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.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.es.md b/1-Introduction/3-fairness/translations/README.es.md index be09df6b..d6c71df9 100644 --- a/1-Introduction/3-fairness/translations/README.es.md +++ b/1-Introduction/3-fairness/translations/README.es.md @@ -3,7 +3,7 @@ ![Resumen de justicia en el aprendizaje automático en un sketchnote](../../../sketchnotes/ml-fairness.png) > Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=es) ## Introducción @@ -183,7 +183,7 @@ Para prevenir que los sesgos sean introducidos en primer lugar, debemos: Piensa en escenarios de la vida real donde la injusticia es evidente en la construcción y uso de modelos. ¿Qué más debemos considerar? -## [Cuestionario posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=es) +## [Cuestionario posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=es) ## Revisión y autoestudio En esta lección has aprendido algunos de los conceptos básicos de justicia e injusticia en el aprendizaje automático. diff --git a/1-Introduction/3-fairness/translations/README.fr.md b/1-Introduction/3-fairness/translations/README.fr.md index c7735721..73e2ef52 100644 --- a/1-Introduction/3-fairness/translations/README.fr.md +++ b/1-Introduction/3-fairness/translations/README.fr.md @@ -3,7 +3,7 @@ ![Résumé de l'équité dans le Machine Learning dans un sketchnote](../../../sketchnotes/ml-fairness.png) > Sketchnote par [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/?loc=fr) +## [Quiz préalable](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=fr) ## Introduction @@ -184,7 +184,7 @@ Pour éviter que des biais ne soient introduits en premier lieu, nous devrions  Pensez à des scénarios de la vie réelle où l'injustice est évidente dans la construction et l'utilisation de modèles. Que devrions-nous considérer d'autre ? -## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/?loc=fr) +## [Quiz de validation des connaissances](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=fr) ## Révision et auto-apprentissage Dans cette leçon, nous avons appris quelques notions de base sur les concepts d'équité et d'injustice dans le machine learning. diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md index 980cbd88..053960d8 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://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) +## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/) ## Pengantar @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) +## [Quiz Pasca-Pelajaran](https://gentle-hill-034defd0f.1.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 29ab88c3..a9440c90 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://white-water-09ec41f0f.azurestaticapps.net/quiz/5/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/?loc=it) ## Introduzione @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/6/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/?loc=it) ## 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 ffa878c1..e5a3d21e 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://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ja) ## イントロダクション @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ja) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.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.ko.md b/1-Introduction/3-fairness/translations/README.ko.md index 7cbc8e35..4718dcc1 100644 --- a/1-Introduction/3-fairness/translations/README.ko.md +++ b/1-Introduction/3-fairness/translations/README.ko.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) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/) ## 소개 @@ -185,7 +185,7 @@ AI와 머신러닝의 공정성을 보장하는 건 계속 복잡한 사회기 모델을 구축하고 사용하면서 불공정한 실-생활 시나리오를 생각해보세요. 어떻게 고려해야 하나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/) ## 검토 & 자기주도 학습 diff --git a/1-Introduction/3-fairness/translations/README.pt-br.md b/1-Introduction/3-fairness/translations/README.pt-br.md index 64a0cffc..9bb5f629 100644 --- a/1-Introduction/3-fairness/translations/README.pt-br.md +++ b/1-Introduction/3-fairness/translations/README.pt-br.md @@ -3,7 +3,7 @@ ![Resumo de imparcialidade no Machine Learning em um sketchnote](../../../sketchnotes/ml-fairness.png) > Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ptbr) +## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5?loc=ptbr) ## Introdução @@ -182,7 +182,7 @@ Para evitar que preconceitos sejam introduzidos em primeiro lugar, devemos: Pense em cenários da vida real onde a injustiça é evidente na construção e uso de modelos. O que mais devemos considerar? -## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ptbr) +## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6?loc=ptbr) ## Revisão e Autoestudo diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md index 5eec4587..b8f6fa3d 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://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) +## [课前测验](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) +## [课后测验](https://gentle-hill-034defd0f.1.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/3-fairness/translations/README.zh-tw.md b/1-Introduction/3-fairness/translations/README.zh-tw.md index 56df3122..db84f46d 100644 --- a/1-Introduction/3-fairness/translations/README.zh-tw.md +++ b/1-Introduction/3-fairness/translations/README.zh-tw.md @@ -2,7 +2,7 @@ ![機器學習中的公平性概述](../../../sketchnotes/ml-fairness.png) > 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/) +## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/5/) ## 介紹 @@ -181,7 +181,7 @@ 想想現實生活中的場景，在模型構建和使用中明顯存在不公平。我們還應該考慮什麽？ -## [課後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/) +## [課後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/6/) ## 復習與自學 在本課中，你學習了機器學習中公平和不公平概念的一些基礎知識。 diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md index 3b46b036..01e40fac 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.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 eb7de48e..e5458c13 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=es) +## [Cuestionario previo a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=es) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=es) +## [Cuestionario posterior a la conferencia](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=es) ## 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 37e0dab7..47e7c5b8 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) +## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) +## [Quiz Pra-Pelajaran](https://gentle-hill-034defd0f.1.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 0f7a4548..b6602f98 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/8/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/?loc=it) ## 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 8f30315b..e6880689 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ja) +## [講義前の小テスト](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ja) +## [講義後の小テスト](https://gentle-hill-034defd0f.1.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.ko.md b/1-Introduction/4-techniques-of-ML/translations/README.ko.md index 2ea97928..7126a3a4 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.ko.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.ko.md @@ -5,7 +5,7 @@ - 머신러닝을 받쳐주는 프로세스를 고수준에서 이해합니다. - 'models', 'predictions', 그리고 'training data'와 같은 기초 개념을 탐색합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/) ## 소개 @@ -103,7 +103,7 @@ feature는 데이터의 측정할 수 있는 속성입니다. 많은 데이터 ML 실무자의 단계를 반영한 플로우를 그려보세요. 프로세스에서 지금 어디에 있는 지 보이나요? 어려운 내용을 예상할 수 있나요? 어떤게 쉬울까요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/) ## 검토 & 자기주도 학습 diff --git a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md index 345f4e61..935dbe62 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.pt-br.md @@ -5,7 +5,7 @@ O processo de construção, uso e manutenção de modelos de machine learning e - Compreender os processos que sustentam o aprendizado de máquina em alto nível. - Explorar conceitos básicos como 'modelos', 'previsões' e 'dados de treinamento'.. -## [Questionário pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ptbr) +## [Questionário pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7?loc=ptbr) ## Introdução @@ -103,7 +103,7 @@ Nessas lições, você descobrirá como usar essas etapas para preparar, criar, Desenhe um fluxograma refletindo as etapas de um praticante de ML. Onde você se vê agora no processo? Onde você prevê que encontrará dificuldade? O que parece fácil para você? -## [Questionário pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ptbr) +## [Questionário pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8?loc=ptbr) ## Revisão e Autoestudo 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 f2b581ca..40f1e669 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://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) +## [课前测验](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) +## [阅读后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/) ## 复习与自学 diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md index 7f7daf08..8d1222be 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-tw.md @@ -6,7 +6,7 @@ - 在高層次上理解支持機器學習的過程。 - 探索基本概念，例如「模型」、「預測」和「訓練數據」。 -## [課前測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/) +## [課前測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/7/) ## 介紹 在較高的層次上，創建機器學習（ML）過程的工藝包括許多步驟： @@ -100,7 +100,7 @@ 畫一個流程圖，反映ML的步驟。在這個過程中，你認為自己現在在哪裏？你預測你在哪裏會遇到困難？什麽對你來說很容易？ -## [閱讀後測驗](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/) +## [閱讀後測驗](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/8/) ## 復習與自學 diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index ee7c72f5..fdd22b69 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://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/) > ### [This lesson is available in R!](./solution/R/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://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.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 cd30700c..26cb6eee 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://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [Kuis Pra-ceramah](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.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 25e726c2..dba63593 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://white-water-09ec41f0f.azurestaticapps.net/quiz/9/?loc=it) +## [Qui Pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/10/?loc=it) +## [Qui post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/?loc=it) ## 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 f7005978..626f4714 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://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja) +## [講義前クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ja) ## イントロダクション @@ -205,7 +205,7 @@ Scikit-learnは、モデルを構築し、評価を行って実際に利用す ## 🚀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://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja) +## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ja) ## レビュー & 自主学習 diff --git a/2-Regression/1-Tools/translations/README.ko.md b/2-Regression/1-Tools/translations/README.ko.md index 6ae34643..040401b0 100644 --- a/2-Regression/1-Tools/translations/README.ko.md +++ b/2-Regression/1-Tools/translations/README.ko.md @@ -4,7 +4,7 @@ > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/) ## 소개 @@ -200,7 +200,7 @@ Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해 이 데이터셋은 다른 변수를 Plot 합니다. 힌트: 이 라인을 수정합니다: `X = X[:, np.newaxis, 2]`. 이 데이터셋의 타겟이 주어질 때, 질병으로 당뇨가 진행되면 어떤 것을 탐색할 수 있나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/) ## 검토 & 자기주도 학습 diff --git a/2-Regression/1-Tools/translations/README.pt-br.md b/2-Regression/1-Tools/translations/README.pt-br.md index 1145b334..fbbc7748 100644 --- a/2-Regression/1-Tools/translations/README.pt-br.md +++ b/2-Regression/1-Tools/translations/README.pt-br.md @@ -4,7 +4,7 @@ > _Sketchnote_ por [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9?loc=ptbr) > ### [Esta lição está disponível em R!](../solution/R/lesson_1-R.ipynb) @@ -200,7 +200,7 @@ Parabéns, usando um conjunto de dados, você construiu seu primeiro modelo de r ## 🚀Desafio Plote uma variável diferente desse mesmo conjunto de dados. Dica: edite a linha: `X = X[:, np.newaxis, 2]`. Dado o conjunto de dados alvo, o que pode ser descoberto sobre o progresso da diabetes como uma doença? -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/2-Regression/1-Tools/translations/README.pt.md b/2-Regression/1-Tools/translations/README.pt.md index 5635fed6..bc18ee02 100644 --- a/2-Regression/1-Tools/translations/README.pt.md +++ b/2-Regression/1-Tools/translations/README.pt.md @@ -5,7 +5,7 @@ > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Questionário pré-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [Questionário pré-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/) > ### [Esta lição está disponível em R!](./solution/R/lesson_1-R.ipynb) @@ -202,7 +202,7 @@ Parabéns, construíste o teu primeiro modelo linear de regressão, criaste uma ## 🚀Challenge Defina uma variável diferente deste conjunto de dados. Dica: edite esta linha:`X = X[:, np.newaxis, 2]`. Tendo em conta o objetivo deste conjunto de dados, o que é que consegue descobrir sobre a progressão da diabetes como uma doença? -## [Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/) ## Review & Self Study diff --git a/2-Regression/1-Tools/translations/README.tr.md b/2-Regression/1-Tools/translations/README.tr.md index c391ee75..561ab28b 100644 --- a/2-Regression/1-Tools/translations/README.tr.md +++ b/2-Regression/1-Tools/translations/README.tr.md @@ -4,7 +4,7 @@ > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [Ders öncesi quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [Ders öncesi quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/) > ### [R dili ile bu dersin içeriği!](././solution/R/lesson_1-R.ipynb) @@ -197,7 +197,7 @@ Tebrikler, ilk doğrusal regresyon modelinizi oluşturdunuz, onunla bir tahmin o ## 🚀Challenge Bu veri kümesinden farklı bir değişken çizin. İpucu: bu satırı düzenleyin: `X = X[:, np.newaxis, 2]`. Bu veri setinin hedefi göz önüne alındığında, diyabetin bir hastalık olarak ilerlemesi hakkında neler keşfedebilirsiniz? -## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/) ## İnceleme ve Bireysel Çalışma diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md index 8f0b8c62..2fc162d6 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://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [课前测](https://gentle-hill-034defd0f.1.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！ @@ -194,7 +194,7 @@ Scikit-learn 使构建模型和评估它们的使用变得简单。它主要侧 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://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/) ## 复习与自学 diff --git a/2-Regression/1-Tools/translations/README.zh-tw.md b/2-Regression/1-Tools/translations/README.zh-tw.md index 79b8aad1..886aea2e 100644 --- a/2-Regression/1-Tools/translations/README.zh-tw.md +++ b/2-Regression/1-Tools/translations/README.zh-tw.md @@ -4,7 +4,7 @@ > 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/) +## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/9/) ## 介紹 @@ -195,7 +195,7 @@ Scikit-learn 使構建模型和評估它們的使用變得簡單。它主要側 從這個數據集中繪製一個不同的變量。提示：編輯這一行：`X = X[:, np.newaxis, 2]`。鑒於此數據集的目標，你能夠發現糖尿病作為一種疾病的進展情況嗎？ -## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/) +## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/10/) ## 復習與自學 diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index 7c84166f..939be63e 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://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/) > ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb) @@ -196,7 +196,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/) ## Review & Self Study diff --git a/2-Regression/2-Data/translations/README.es.md b/2-Regression/2-Data/translations/README.es.md index 9ce762e8..2c188cfe 100644 --- a/2-Regression/2-Data/translations/README.es.md +++ b/2-Regression/2-Data/translations/README.es.md @@ -4,7 +4,7 @@ Infografía por [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=es) > ### [Esta lección se encuentra disponible en R!](../solution/R/lesson_2-R.ipynb) @@ -196,7 +196,7 @@ Para obtener gráficas para mostrar datos útiles, necesitas agrupar los datos d Explora los distintos tipos de visualización que ofrece Matplotlib. ¿Qué tipos son los más apropiados para problemas de regresión? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=es) ## Revisión y autoestudio diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md index 9d8b5f18..9e0f05d6 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://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.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 b9882184..d0f51a57 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://white-water-09ec41f0f.azurestaticapps.net/quiz/11/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/12/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/?loc=it) ## 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 ddd01a77..a5f7dcf4 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://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ja) +## [講義前のクイズ](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja) +## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ja) ## レビュー & 自主学習 diff --git a/2-Regression/2-Data/translations/README.ko.md b/2-Regression/2-Data/translations/README.ko.md index 64ddc721..5bf393f7 100644 --- a/2-Regression/2-Data/translations/README.ko.md +++ b/2-Regression/2-Data/translations/README.ko.md @@ -4,7 +4,7 @@ > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/) ## 소개 @@ -191,7 +191,7 @@ Jupyter notebooks에서 잘 작동하는 데이터 시각화 라이브러리는 Matplotlib에서 제공하는 다양한 시각화 타입을 찾아보세요. regression 문제에 가장 적당한 타입은 무엇인가요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/) ## 검토 & 자기주도 학습 diff --git a/2-Regression/2-Data/translations/README.pt-br.md b/2-Regression/2-Data/translations/README.pt-br.md index e5fdc94c..7ba9fe85 100644 --- a/2-Regression/2-Data/translations/README.pt-br.md +++ b/2-Regression/2-Data/translations/README.pt-br.md @@ -4,7 +4,7 @@ Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11?loc=ptbr) > ### [Esta liçao está disponível em R!](../solution/R/lesson_2-R.ipynb) @@ -197,7 +197,7 @@ Para fazer com que os gráficos exibam dados úteis, você precisa agrupar os da Explore os diferentes tipos de visualização que o Matplotlib oferece. Quais tipos são mais adequados para problemas de regressão? -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/2-Regression/2-Data/translations/README.pt.md b/2-Regression/2-Data/translations/README.pt.md index 6cd25fad..e2869a15 100644 --- a/2-Regression/2-Data/translations/README.pt.md +++ b/2-Regression/2-Data/translations/README.pt.md @@ -4,7 +4,7 @@ Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Teste de pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [Teste de pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/) > ### [Esta lição está disponível em R!](./solution/R/lesson_2-R.ipynb) @@ -196,7 +196,7 @@ Esta é uma visualização de dados mais útil! Parece indicar que o preço mais Explore os diferentes tipos de visualização que o Matplotlib oferece. Que tipos são mais apropriados para problemas de regressão? -## [Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/) ## Revisão e Estudo Automático diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md index c31c8726..f204e1e7 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://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/) ## 介绍 @@ -192,7 +192,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/) ## 复习与自学 diff --git a/2-Regression/2-Data/translations/README.zh-tw.md b/2-Regression/2-Data/translations/README.zh-tw.md index c3d92fde..f9bfbb99 100644 --- a/2-Regression/2-Data/translations/README.zh-tw.md +++ b/2-Regression/2-Data/translations/README.zh-tw.md @@ -3,7 +3,7 @@ ![數據可視化信息圖](../images/data-visualization.png) > 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/) +## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/11/) ## 介紹 @@ -192,7 +192,7 @@ 探索 Matplotlib 提供的不同類型的可視化。哪種類型最適合回歸問題？ -## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/) +## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/12/) ## 復習與自學 diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md index 40a07fce..b7e07bac 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://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/) > ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb) ### Introduction @@ -326,7 +326,7 @@ This should give us the best determination coefficient of almost 97%, and MSE=2. Test several different variables in this notebook to see how correlation corresponds to model accuracy. -## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## Review & Self Study diff --git a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index 4580481d..ba156fb6 100644 --- a/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/2-Regression/3-Linear/solution/R/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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n", + "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/)\n", "\n", "## **Review & Self Study**\n", "\n", diff --git a/2-Regression/3-Linear/solution/R/lesson_3.Rmd b/2-Regression/3-Linear/solution/R/lesson_3.Rmd index 7997b0d8..712011f4 100644 --- a/2-Regression/3-Linear/solution/R/lesson_3.Rmd +++ b/2-Regression/3-Linear/solution/R/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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## **Review & Self Study** diff --git a/2-Regression/3-Linear/translations/README.es.md b/2-Regression/3-Linear/translations/README.es.md index c76aec95..db79c5af 100644 --- a/2-Regression/3-Linear/translations/README.es.md +++ b/2-Regression/3-Linear/translations/README.es.md @@ -3,7 +3,7 @@ ![Infografía de regresión lineal vs polinomial](./images/linear-polynomial.png) > Infografía de [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=es) > ### [¡Esta lección está disponible en R!](../solution/R/lesson_3-R.ipynb) @@ -331,7 +331,7 @@ Llama a `predict()` para hacer una predicción: Prueba variables diferentes en este notebook para ver cómo la correlación corresponde a la precisión del modelo. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=es) ## Revisión y auto-estudio diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md index ce2ee409..b454c4db 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://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.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 a95d005e..f2c0a295 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://white-water-09ec41f0f.azurestaticapps.net/quiz/13/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/?loc=it) ### 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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/?loc=it) ## 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 2dbc0f32..0bebdb96 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://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [講義前のクイズ](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## レビュー & 自主学習 diff --git a/2-Regression/3-Linear/translations/README.ko.md b/2-Regression/3-Linear/translations/README.ko.md index 57ba3201..baa9abf8 100644 --- a/2-Regression/3-Linear/translations/README.ko.md +++ b/2-Regression/3-Linear/translations/README.ko.md @@ -3,7 +3,7 @@ ![Linear vs polynomial regression infographic](.././images/linear-polynomial.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/) ### 소개 @@ -327,7 +327,7 @@ Scikit-learn에는 polynomial regression 모델을 만들 때 도움을 받을 노트북에서 다른 변수를 테스트하면서 상관 관계가 모델 정확도에 어떻게 대응되는 지 봅니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## 검토 & 자기주도 학습 diff --git a/2-Regression/3-Linear/translations/README.pt-br.md b/2-Regression/3-Linear/translations/README.pt-br.md index a0a5c46c..81b137c7 100644 --- a/2-Regression/3-Linear/translations/README.pt-br.md +++ b/2-Regression/3-Linear/translations/README.pt-br.md @@ -3,7 +3,7 @@ ![Infográfico de regressão linear versus polinomial](../images/linear-polynomial.png) > Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13?loc=ptbr) > ### [Esta liçao está disponível em R!](../solution/R/lesson_3-R.ipynb) @@ -331,7 +331,7 @@ E se esse modelo for melhor que o anterior usando o mesmo conjunto de dados, voc Teste variáveis diferentes neste _notebook_ para ver como a correlação corresponde à acurácia do modelo. -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/2-Regression/3-Linear/translations/README.pt.md b/2-Regression/3-Linear/translations/README.pt.md index c3386d96..68f62714 100644 --- a/2-Regression/3-Linear/translations/README.pt.md +++ b/2-Regression/3-Linear/translations/README.pt.md @@ -2,7 +2,7 @@ ![Regressão linear vs polinomial infográfica](./images/linear-polynomial.png) > Infográfico de [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Questionário pré-seleção](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [Questionário pré-seleção](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/) > ### [Esta lição está disponível em R!](./solution/R/lesson_3-R.ipynb) ### Introdução @@ -321,7 +321,7 @@ Faz sentido, dado o enredo! E, se este é um modelo melhor do que o anterior, ol ## 🚀 desafio Teste várias variáveis diferentes neste bloco de notas para ver como a correlação corresponde à precisão do modelo. -##[Questionário pós-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +##[Questionário pós-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## Revisão e Estudo Automático diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md index bffa0e22..94e59545 100644 --- a/2-Regression/3-Linear/translations/README.zh-cn.md +++ b/2-Regression/3-Linear/translations/README.zh-cn.md @@ -3,7 +3,7 @@ ![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://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/) ### 介绍 @@ -330,7 +330,7 @@ Scikit-learn 包含一个用于构建多项式回归模型的有用 API - `make_ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## 复习与自学 diff --git a/2-Regression/3-Linear/translations/README.zh-tw.md b/2-Regression/3-Linear/translations/README.zh-tw.md index a5a78ed4..4592c2f1 100644 --- a/2-Regression/3-Linear/translations/README.zh-tw.md +++ b/2-Regression/3-Linear/translations/README.zh-tw.md @@ -4,7 +4,7 @@ > 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/) +## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/13/) ### 介紹 @@ -331,7 +331,7 @@ Scikit-learn 包含一個用於構建多項式回歸模型的有用 API - `make_ 在此 notebook 中測試幾個不同的變量，以查看相關性與模型準確性的對應關系。 -## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/) +## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/14/) ## 復習與自學 diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 9ff52164..6da94915 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://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) > ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb) @@ -298,7 +298,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## Review & Self Study diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index 25320f05..c82e79f1 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -45,7 +45,7 @@ { "cell_type": "markdown", "source": [ - "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n", + "#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)**\n", "\n", "#### Introduction\n", "\n", diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 0eeadaf3..26ac170c 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -14,7 +14,7 @@ output: ![Infographic by Dasani Madipalli](../../images/logistic-linear.png){width="600"} -#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)** +#### ** [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/)** #### Introduction diff --git a/2-Regression/4-Logistic/translations/README.es.md b/2-Regression/4-Logistic/translations/README.es.md index e66ef072..d0e9913d 100644 --- a/2-Regression/4-Logistic/translations/README.es.md +++ b/2-Regression/4-Logistic/translations/README.es.md @@ -3,7 +3,7 @@ ![Infografía de regresiones lineal vs logística](../images/logistic-linear.png) > Infografía de [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=es) > ### [Esta lección se encuentra disponible en R!](../solution/R/lesson_4-R.ipynb) @@ -302,7 +302,7 @@ En futuras lecciones de clasificación, aprenderás cómo iterar para mejorar lo ¡Hay mucho más para desempacar respecto a la regresión logística! Pero la mejor forma de aprender es experimentar. Encuentra un conjunto de datos que se preste para este tipo de análisis y construye un modelo con él. ¿Qué aprendes? tipo: prueba [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) por conjuntos de datos interesantes. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=es) ## Revisión & autoestudio diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md index 553205d7..5ec38e2d 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://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [Kuis pra-ceramah](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [Kuis pasca-ceramah](https://gentle-hill-034defd0f.1.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 943a68c7..c4e26979 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://white-water-09ec41f0f.azurestaticapps.net/quiz/15/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/16/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/?loc=it) ## 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 662a1eaf..0158d9ed 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://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [講義前のクイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) ## イントロダクション @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [講義後クイズ](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## レビュー & 自主学習 diff --git a/2-Regression/4-Logistic/translations/README.ko.md b/2-Regression/4-Logistic/translations/README.ko.md index 1bca8962..5a6ac4a3 100644 --- a/2-Regression/4-Logistic/translations/README.ko.md +++ b/2-Regression/4-Logistic/translations/README.ko.md @@ -3,7 +3,7 @@ ![Logistic vs. linear regression infographic](.././images/logistic-linear.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) ## 소개 @@ -300,7 +300,7 @@ classifications에 대한 이후 강의에서, 모델의 스코어를 개선하 logistic regression과 관련해서 풀어야할 내용이 더 있습니다! 하지만 배우기 좋은 방식은 실험입니다. 이런 분석에 적당한 데이터셋을 찾아서 모델을 만듭니다. 무엇을 배우나요? 팁: 흥미로운 데이터셋으로 [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets)에서 시도해보세요. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## 검토 & 자기주도 학습 diff --git a/2-Regression/4-Logistic/translations/README.pt-br.md b/2-Regression/4-Logistic/translations/README.pt-br.md index 3633bc10..b62f4a33 100644 --- a/2-Regression/4-Logistic/translations/README.pt-br.md +++ b/2-Regression/4-Logistic/translations/README.pt-br.md @@ -2,7 +2,7 @@ ![Infográfico de regressão logística versus regressão linear](../images/logistic-linear.png) > Infográfico por [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15?loc=ptbr) > ### [Esta liçao está disponível em R!](../solution/R/lesson_4-R.ipynb) @@ -300,7 +300,7 @@ Em outras lições sobre classificação, você aprenderá como iterar para melh Ainda há muito sobre regressão logística! E a melhor maneira de aprender é experimentando. Encontre um conjunto de dados para este tipo de análise e construa um modelo com ele. O que você aprendeu? dica: tente o [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para conjuntos de dados interessantes. -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/2-Regression/4-Logistic/translations/README.pt.md b/2-Regression/4-Logistic/translations/README.pt.md index 2eae5c06..82929a23 100644 --- a/2-Regression/4-Logistic/translations/README.pt.md +++ b/2-Regression/4-Logistic/translations/README.pt.md @@ -2,7 +2,7 @@ ![Infográfico logístico vs. regressão linear](../images/logistic-linear.png) > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [Questionário pré-palestra](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [Questionário pré-palestra](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) > ### [Esta lição está disponível em R!](./solution/R/lesson_4-R.ipynb) @@ -291,7 +291,7 @@ Em lições futuras sobre classificações, você aprenderá a iterar para melho Há muito mais a desempacotar em relação à regressão logística! Mas a melhor maneira de aprender é experimentar. Encontre um conjunto de dados que se preste a esse tipo de análise e construa um modelo com ele. O que você aprende? dica: tente [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) para obter conjuntos de dados interessantes. -## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## Análise e autoestudo diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md index 44994663..fb80b284 100644 --- a/2-Regression/4-Logistic/translations/README.zh-cn.md +++ b/2-Regression/4-Logistic/translations/README.zh-cn.md @@ -3,7 +3,7 @@ ![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://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [课前测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) ## 介绍 @@ -289,7 +289,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [课后测](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## 复习与自学 diff --git a/2-Regression/4-Logistic/translations/README.zh-tw.md b/2-Regression/4-Logistic/translations/README.zh-tw.md index b8789d35..ab481098 100644 --- a/2-Regression/4-Logistic/translations/README.zh-tw.md +++ b/2-Regression/4-Logistic/translations/README.zh-tw.md @@ -4,7 +4,7 @@ > 作者 [Dasani Madipalli](https://twitter.com/dasani_decoded) -## [課前測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/) +## [課前測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/15/) ## 介紹 @@ -290,7 +290,7 @@ print(auc) 關於邏輯回歸，還有很多東西需要解開！但最好的學習方法是實驗。找到適合此類分析的數據集並用它構建模型。你學到了什麽？小貼士：嘗試 [Kaggle](https://kaggle.com) 獲取有趣的數據集。 -## [課後測](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/) +## [課後測](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/16/) ## 復習與自學 diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index 5af9f650..7af22fca 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://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/) ## Review & Self Study diff --git a/3-Web-App/1-Web-App/translations/README.es.md b/3-Web-App/1-Web-App/translations/README.es.md index 5a825b70..5eb396b4 100644 --- a/3-Web-App/1-Web-App/translations/README.es.md +++ b/3-Web-App/1-Web-App/translations/README.es.md @@ -11,7 +11,7 @@ Continuaremos nuestro uso de notebooks para limpiar los datos y entrenar nuestro Para hacer esto, necesitas construir una aplicación web usando Flask. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=es) ## Construyendo una aplicación @@ -335,7 +335,7 @@ En un entorno profesional, puedes ver cómo la buena comunicación es necesaria En lugar de trabajar en un notebook e importar el modelo a una aplicación Flask, ¡podrías entrenar el modelo directo en la aplicación Flask! Intenta convertir tu código Python en el notebook, quizá después que tus datos sean limpiados, para entrenar el modelo desde la aplicación en una ruta llamada `train`. ¿Cuáles son los pros y contras de seguir este método? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=es) ## Revisión y autoestudio 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 9d5fa430..fec92dec 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://white-water-09ec41f0f.azurestaticapps.net/quiz/17/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/18/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/?loc=it) ## 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 ba9f9170..7d18f6bf 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://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ja) +## [講義前の小テスト](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ja) +## [講義後の小テスト](https://gentle-hill-034defd0f.1.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.ko.md b/3-Web-App/1-Web-App/translations/README.ko.md index 24330063..a25e5be6 100644 --- a/3-Web-App/1-Web-App/translations/README.ko.md +++ b/3-Web-App/1-Web-App/translations/README.ko.md @@ -11,7 +11,7 @@ 이러면, Flask로 웹 앱을 만들어야 합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/) ## 앱 만들기 @@ -335,7 +335,7 @@ Flask와 pickled 모델과 같이, 모델을 사용하는 이 방식은, 비교 노트북에서 작성하고 Flask 앱에서 모델을 가져오는 대신, Flask 앱에서 바로 모델을 훈련할 수 있습니다! 어쩌면 데이터를 정리하고, 노트북에서 Python 코드로 변환해서, `train`이라고 불리는 라우터로 앱에서 모델을 훈련합니다. 이러한 방식을 추구했을 때 장점과 단점은 무엇인가요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/) ## 검토 & 자기주도 학습 diff --git a/3-Web-App/1-Web-App/translations/README.pt-br.md b/3-Web-App/1-Web-App/translations/README.pt-br.md index 0d744629..7fdf3a7f 100644 --- a/3-Web-App/1-Web-App/translations/README.pt-br.md +++ b/3-Web-App/1-Web-App/translations/README.pt-br.md @@ -11,7 +11,7 @@ Continuaremos nosso uso de notebooks para limpar dados e treinar nosso modelo, m Para fazer isso, você precisa construir um aplicativo da web usando o Flask. -## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ptbr) +## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17?loc=ptbr) ## Construindo um aplicativo @@ -337,7 +337,7 @@ Em um ambiente profissional, você pode ver como uma boa comunicação é necess Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, você pode treinar o modelo diretamente no aplicativo Flask! Tente converter seu código Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais são os prós e os contras de seguir esse método? -## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ptbr) +## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18?loc=ptbr) ## Revisão e autoestudo diff --git a/3-Web-App/1-Web-App/translations/README.pt.md b/3-Web-App/1-Web-App/translations/README.pt.md index 00b18c02..92c02107 100644 --- a/3-Web-App/1-Web-App/translations/README.pt.md +++ b/3-Web-App/1-Web-App/translations/README.pt.md @@ -11,7 +11,7 @@ Continuaremos a usar notebooks para limpar dados e treinar nosso modelo, mas voc Para fazer isso, você precisa construir um aplicativo Web usando Flask. -## [Teste de pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) +## [Teste de pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/17/) ## Criando um aplicativo @@ -336,7 +336,7 @@ Em um ambiente profissional, você pode ver como uma boa comunicação é necess Em vez de trabalhar em um notebook e importar o modelo para o aplicativo Flask, você poderia treinar o modelo dentro do aplicativo Flask! Tente converter seu código Python no notebook, talvez depois que seus dados forem limpos, para treinar o modelo de dentro do aplicativo em uma rota chamada `train`. Quais são os prós e contras de se buscar esse método? -## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) +## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/18/) ## Análise e autoestudo 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 c6409808..faf92e1a 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://white-water-09ec41f0f.azurestaticapps.net/quiz/17/) +## [课前测](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/18/) +## [课后测](https://gentle-hill-034defd0f.1.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 03b0ba97..1fd35e8c 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://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/) > ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb) @@ -288,7 +288,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/) ## Review & Self Study diff --git a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb index a0e93536..9fb94e30 100644 --- a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb +++ b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb @@ -50,7 +50,7 @@ "\n", "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.\n", "\n", - "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)\n", + "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/)\n", "\n", "### **Introduction**\n", "\n", @@ -692,7 +692,7 @@ "\r\n", "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?\r\n", "\r\n", - "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)\r\n", + "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/)\r\n", "\r\n", "## **Review & Self Study**\r\n", "\r\n", diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd index f31a7e0b..06959b65 100644 --- a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd +++ b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd @@ -26,7 +26,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) +### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/) ### **Introduction** @@ -403,7 +403,7 @@ This fresh CSV can now be found in the root data folder. 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://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) +## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/) ## **Review & Self Study** diff --git a/4-Classification/1-Introduction/translations/README.es.md b/4-Classification/1-Introduction/translations/README.es.md index 60aa54bc..b1ee4742 100644 --- a/4-Classification/1-Introduction/translations/README.es.md +++ b/4-Classification/1-Introduction/translations/README.es.md @@ -19,7 +19,7 @@ Recuerda: La clasificación utiliza varios algorítmos para determinar otras formas de determinar la clase o etiqueta de un punto de datos. Trabajemos con estos datos de cocina para ver si, al observar un grupo de ingredientes, podemos determinar su cocina u origen. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19?loc=es) > ### [¡Esta lección está disponible en R!](./solution/R/lesson_10-R.ipynb) @@ -288,7 +288,7 @@ Ahora que has limpiado los datos, usa [SMOTE](https://imbalanced-learn.org/dev/r Este plan de estudios contiene varios conjuntos de datos interesantes. Profundiza en los directorios `data` y ve si alguno contiene conjuntos de datos que serían apropiados para clasificación binaria o multiclase. ¿Qué preguntas harías a este conunto de datos? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=es) ## Revisión y autoestudio diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md index 8115bb8d..5bfd7fb0 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://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=it) ### 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://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/?loc=it) ## Revisione e Auto Apprendimento diff --git a/4-Classification/1-Introduction/translations/README.ko.md b/4-Classification/1-Introduction/translations/README.ko.md index c1acee07..abe82d3d 100644 --- a/4-Classification/1-Introduction/translations/README.ko.md +++ b/4-Classification/1-Introduction/translations/README.ko.md @@ -19,7 +19,7 @@ Classification은 regression 기술과 공통점이 많은 [supervised learning] Classification은 다양한 알고리즘으로 데이터 포인트의 라벨 혹은 클래스를 결정할 다른 방식을 고릅니다. 요리 데이터로, 재료 그룹을 찾아서, 전통 요리로 결정할 수 있는지 알아보려 합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/) ### 소개 @@ -287,7 +287,7 @@ Scikit-learn은 해결하고 싶은 문제의 타입에 따라서, 데이터를 해당 커리큘럼은 여러 흥미로운 데이터셋을 포함하고 있습니다. `data` 폴더를 파보면서 binary 또는 multi-class classification에 적당한 데이터셋이 포함되어 있나요? 데이터셋에 어떻게 물어보나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20/) ## 검토 & 자기주도 학습 diff --git a/4-Classification/1-Introduction/translations/README.pt-br.md b/4-Classification/1-Introduction/translations/README.pt-br.md index c4b7b32f..68dd89cf 100644 --- a/4-Classification/1-Introduction/translations/README.pt-br.md +++ b/4-Classification/1-Introduction/translations/README.pt-br.md @@ -19,7 +19,7 @@ Lembre-se: A classificação usa vários algoritmos para determinar outras maneiras de determinar o rótulo ou a classe de um ponto de dados ou objeto. Vamos trabalhar com dados sobre culinária para ver se, observando um grupo de ingredientes, podemos determinar sua culinária de origem. -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/19/?loc=ptbr) > ### [Esta lição está disponível em R!](../solution/R/lesson_10-R.ipynb) @@ -288,7 +288,7 @@ Agora que você limpou os dados, use a [SMOTE](https://imbalanced-learn.org/dev/ Esta lição contém vários _datasets_ interessantes. Explore os arquivos da pasta `data` e veja quais _datasets_ seriam apropriados para classificação binária ou multiclasse. Quais perguntas você faria sobre estes _datasets_? -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/20?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md index e1b32ec9..876c1ce7 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://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=tr) +## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=tr) +## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.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 b1d2862b..70954b6f 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://white-water-09ec41f0f.azurestaticapps.net/quiz/19/) +## [课程前的小问题](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/20/) +## [课后练习](https://gentle-hill-034defd0f.1.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 c1e953ce..7555000c 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://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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. @@ -231,7 +231,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use 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://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/) ## Review & Self Study diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb index 2d88245a..63d42391 100644 --- a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb @@ -33,7 +33,7 @@ "\n", "In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks.\n", "\n", - "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)\n", + "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/)\n", "\n", "### **Preparation**\n", "\n", diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd index c59d5c90..000142ec 100644 --- a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd @@ -14,7 +14,7 @@ output: In this lesson, we'll explore a variety of classifiers to *predict a given national cuisine based on a group of ingredients.* While doing so, we'll learn more about some of the ways that algorithms can be leveraged for classification tasks. -### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) +### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/) ### **Preparation** diff --git a/4-Classification/2-Classifiers-1/translations/README.es.md b/4-Classification/2-Classifiers-1/translations/README.es.md index 64792f79..1568d172 100644 --- a/4-Classification/2-Classifiers-1/translations/README.es.md +++ b/4-Classification/2-Classifiers-1/translations/README.es.md @@ -4,7 +4,7 @@ En esta lección, usarás el conjunto de datos que guardaste en la última lecci Usarás este conjunto de datos con una variedad de clasificadores para _predecir una cocina nacional dada basado en un grupo de ingredientes_. Mientras lo haces, aprenderás más acerca de algunas formas en que los algoritmos pueden ser aprovechados para las tareas de clasificación. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=es) # Preparación @@ -234,7 +234,7 @@ Ya que estás usando un caso multiclase, necesitas elegir qué _esquema_ usar y En esta lección, usaste tus datos limpios para construir un modelo de aprendizaje automático que puede predecir una cocina nacional basado en una serie de ingredientes. Toma un tiempo para leer las diversas opciones que provee Scikit-learn para clasificar los datos. Profundiza en el concepto de 'solucionador' para comprender que sucede detrás de escena. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=es) ## Revisión y autoestudio diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md index 4128c510..ed85e784 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://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/?loc=it) # 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. @@ -232,7 +232,7 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/?loc=it) ## 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.ko.md b/4-Classification/2-Classifiers-1/translations/README.ko.md index e2c5c959..d6db0d07 100644 --- a/4-Classification/2-Classifiers-1/translations/README.ko.md +++ b/4-Classification/2-Classifiers-1/translations/README.ko.md @@ -4,7 +4,7 @@ 다양한 classifiers와 데이터셋을 사용해서 _재료 그룹 기반으로 주어진 국민 요리를 예측_ 합니다. 이러는 동안, classification 작업에 알고리즘을 활용할 몇 방식에 대해 자세히 배워볼 예정입니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/) ## 준비하기 @@ -233,7 +233,7 @@ multiclass 케이스로, 사용할 _scheme_ 와 설정할 _solver_ 를 선택해 이 강의에서, 정리된 데이터로 재료의 시리즈를 기반으로 국민 요리를 예측할 수 있는 머신러닝 모델을 만들었습니다. 시간을 투자해서 Scikit-learn이 데이터를 분류하기 위해 제공하는 다양한 옵션을 읽어봅니다. 무대 뒤에서 생기는 일을 이해하기 위해서 'solver'의 개념을 깊게 파봅니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/) ## 검토 & 자기주도 학습 [this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)에서 logistic regression 뒤의 수학에 대해서 더 자세히 파봅니다. diff --git a/4-Classification/2-Classifiers-1/translations/README.pt-br.md b/4-Classification/2-Classifiers-1/translations/README.pt-br.md index 837bd293..193f8d85 100644 --- a/4-Classification/2-Classifiers-1/translations/README.pt-br.md +++ b/4-Classification/2-Classifiers-1/translations/README.pt-br.md @@ -4,7 +4,7 @@ Nesta lição, você usará o _dataset_ balanceado e tratado que salvou da últi Você usará este _dataset_ com uma variedade de classificadores para _prever uma determinada culinária nacional com base em um grupo de ingredientes_. Enquanto isso, você aprenderá mais sobre algumas das maneiras como os algoritmos podem ser aproveitados para tarefas de classificação. -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21?loc=ptbr) # Preparação @@ -232,7 +232,7 @@ Já que estamos usando um caso multiclasse, você precisa escolher qual _scheme_ Nesta lição, você usou seus dados para construir um modelo de aprendizado de máquina que pode prever uma culinária nacional com base em uma série de ingredientes. Reserve algum tempo para ler as opções que o Scikit-learn oferece para classificar dados. Aprofunde-se no conceito de 'solucionador' para entender o que acontece nos bastidores. -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md index 30f36b13..f59a39a8 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://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=tr) +## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.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. @@ -231,7 +231,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine 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://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=tr) +## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.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 0c08c17f..06761a10 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://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/21/) # 准备工作 @@ -230,7 +230,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/22/) ## 回顾与自学 diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md index 00675169..014a4662 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://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/) ## Review & Self Study diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb index d1a6fbf2..4c22b93e 100644 --- a/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb +++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb @@ -35,7 +35,7 @@ "\n", "In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other.\n", "\n", - "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)\n", + "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/)\n", "\n", "### **Prerequisite**\n", "\n", @@ -619,7 +619,7 @@ "\n", "> In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**.\n", "\n", - "### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)\n", + "### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/)\n", "\n", "### **Review & Self Study**\n", "\n", diff --git a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd index 3a6f6ba4..526b8503 100644 --- a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd +++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd @@ -14,7 +14,7 @@ output: In this second classification lesson, we will explore `more ways` to classify categorical data. We will also learn about the ramifications for choosing one classifier over the other. -### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) +### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/) ### **Prerequisite** @@ -433,7 +433,7 @@ To find out more about a particular model and its parameters, use: `help("model" > In practice, we usually *estimate* the *best values* for these by training many models on a `simulated data set` and measuring how well all these models perform. This process is called **tuning**. -### [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) +### [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/) ### **Review & Self Study** diff --git a/4-Classification/3-Classifiers-2/translations/README.es.md b/4-Classification/3-Classifiers-2/translations/README.es.md index 1e81e46c..bd16d23f 100644 --- a/4-Classification/3-Classifiers-2/translations/README.es.md +++ b/4-Classification/3-Classifiers-2/translations/README.es.md @@ -2,7 +2,7 @@ En esta segunda lección de clasificación, explorarás más formas de clasificar datos numéricos. También aprenderás acerca de las ramificaciones para elegir un clasificador en lugar de otro. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=es) ### Prerrequisito @@ -223,7 +223,7 @@ Este método de aprendizaje automático "combina las predicciones de varios esti Cada una de estas técnicas tiene un gran número de parámetros que puedes modificar. Investiga los parámetros predeterminados de cada uno y piensa en lo que significaría el ajuste de estos parámetros para la calidad del modelo. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=es) ## Revisión y autoestudio diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md index 5294a706..8f4fdd03 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://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/?loc=it) ### 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://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=it) ## Revisione e Auto Apprendimento diff --git a/4-Classification/3-Classifiers-2/translations/README.ko.md b/4-Classification/3-Classifiers-2/translations/README.ko.md index 9438c430..78b67268 100644 --- a/4-Classification/3-Classifiers-2/translations/README.ko.md +++ b/4-Classification/3-Classifiers-2/translations/README.ko.md @@ -2,7 +2,7 @@ 두번째 classification 강의에서, 숫자 데이터를 분류하는 더 많은 방식을 알아봅니다. 다른 것보다 하나의 classifier를 선택하는 파급효과도 배우게 됩니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/) ### 필요 조건 @@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199 각 기술에는 트윅할 수 있는 많은 수의 파라미터가 존재합니다. 각 기본 파라미터를 조사하고 파라미터를 조절헤서 모델 품질에 어떤 의미가 부여되는지 생각합니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/) ## 검토 & 자기주도 학습 diff --git a/4-Classification/3-Classifiers-2/translations/README.pt-br.md b/4-Classification/3-Classifiers-2/translations/README.pt-br.md index ec01ef5c..4315d9e3 100644 --- a/4-Classification/3-Classifiers-2/translations/README.pt-br.md +++ b/4-Classification/3-Classifiers-2/translations/README.pt-br.md @@ -2,7 +2,7 @@ Nesta segunda lição de classificação, você explorará outras maneiras de classificar dados numéricos. Você também aprenderá sobre as ramificações para escolher um classificador em vez de outro. -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23?loc=ptbr) ### Pré-requisito @@ -224,7 +224,7 @@ Este método de arendizado de máquina "combina as previsões de vários estimad Cada uma dessas técnicas possui um grande número de parâmetros. Pesquise os parâmetros padrão de cada um e pense no que o ajuste desses parâmetros significaria para a qualidade do modelo. -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md index 56c1e11e..aba0b99b 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://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=tr) +## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr) +## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/?loc=tr) ## Gözden Geçirme & Kendi Kendine Çalışma diff --git a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md index f26f2a42..203daf04 100644 --- a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md +++ b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md @@ -2,7 +2,7 @@ 在第二节课程中，您将探索更多方法来对数值数据进行分类。您还将了解选择不同的分类器所带来的结果。 -## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/23/) ### 先决条件 @@ -224,7 +224,7 @@ weighted avg 0.73 0.72 0.72 1199 这些技术方法每个都有很多能够让您微调的参数。研究每一个的默认参数，并思考调整这些参数对模型质量有何意义。 -## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/24/) ## 回顾与自学 diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md index ef63b2b6..baf63da1 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: Jen Looper builds a web app using classified cuisine data -## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/) In this lesson you will learn: @@ -299,7 +299,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/26/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/) ## Review & Self Study diff --git a/4-Classification/4-Applied/translations/README.es.md b/4-Classification/4-Applied/translations/README.es.md index 60962210..161db84c 100644 --- a/4-Classification/4-Applied/translations/README.es.md +++ b/4-Classification/4-Applied/translations/README.es.md @@ -8,7 +8,7 @@ Uno de los usos prácticos más útiles del aprendizaje automático es construir > 🎥 Haz clic en la imagen de arriba para ver el video: Jen Looper construye una aplicación web usando los datos clasificados de cocina. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=es) En esta lección aprenderás: @@ -301,7 +301,7 @@ Felicidades, has creado una aplicación de 'recomendación' con pocos campos. ¡ Tu aplicación web es mínima, así que continua construyéndola usando los ingredientes y sus índices de los datos [ingredient_indexes](../../data/ingredient_indexes.csv). ¿Qué combinaciones de sabor funcionan para crear un determinado platillo nacional? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=es) ## Revisión y autoestudio diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md index b106ba00..4e4816dc 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 -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/?loc=it) 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://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=it) ## Revisione e Auto Apprendimento diff --git a/4-Classification/4-Applied/translations/README.ko.md b/4-Classification/4-Applied/translations/README.ko.md index d4493a9e..3d9e2794 100644 --- a/4-Classification/4-Applied/translations/README.ko.md +++ b/4-Classification/4-Applied/translations/README.ko.md @@ -8,7 +8,7 @@ > 🎥 영상 보려면 이미지 클릭 -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/) 이 강의에서 다음을 배우게 됩니다: @@ -322,7 +322,7 @@ index.html 파일의 폴더에서 Visual Studio Code로 터미널 세션을 엽 이 웹 앱은 매우 작아서, [ingredient_indexes](../../data/ingredient_indexes.csv) 데이터에서 성분과 인덱스로 계속 만듭니다. 주어진 국민 요리를 만드려면 어떤 풍미 조합으로 작업해야 되나요? -## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/) ## 검토 & 자기주도 학습 diff --git a/4-Classification/4-Applied/translations/README.pt-br.md b/4-Classification/4-Applied/translations/README.pt-br.md index f66463dc..f7fc7214 100644 --- a/4-Classification/4-Applied/translations/README.pt-br.md +++ b/4-Classification/4-Applied/translations/README.pt-br.md @@ -8,7 +8,7 @@ Um dos usos práticos mais úteis do aprendizado de máquina é criar sistemas d > 🎥 Clique na imagem acima para ver um vídeo -## [Questionário inicial](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25?loc=ptbr) +## [Questionário inicial](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25?loc=ptbr) Nesta lição você aprenderá: @@ -322,7 +322,7 @@ Parabéns, você criou uma aplicação Web de 'recomendação' com alguns campos Sua aplicação é simples, portanto, adicione outros ingredientes observando seus índices na [planilha de ingredientes](../../data/ingredient_indexes.csv). Que combinações de sabores funcionam para criar um determinado prato? -## [Questionário para fixação](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26?loc=ptbr) +## [Questionário para fixação](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26?loc=ptbr) ## Revisão e Auto Aprendizagem diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md index f46f3164..d30610a1 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 -## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=tr) +## [Ders öncesi kısa sınavı](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr) +## [Ders sonrası kısa sınavı](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/?loc=tr) ## Gözden Geçirme & Kendi Kendine Çalışma diff --git a/4-Classification/4-Applied/translations/README.zh-CN.md b/4-Classification/4-Applied/translations/README.zh-CN.md index bc9e4257..eed2a5ca 100644 --- a/4-Classification/4-Applied/translations/README.zh-CN.md +++ b/4-Classification/4-Applied/translations/README.zh-CN.md @@ -7,7 +7,7 @@ > 🎥 点击上面的图片查看视频 -## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/25/) 本节课程中您将会学习： @@ -318,7 +318,7 @@ Netron 是查看您模型的有用工具。 您的 Web 应用程序还很小巧，所以继续使用[配料索引](../../data/ingredient_indexes.csv)中的配料数据和索引数据来构建它吧。用什么样的口味组合才能创造出一道特定的民族菜肴？ -## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/26/) ## 回顾与自学 diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index 37068503..32f07019 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://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/) ## Review & Self Study diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb index 66f4403b..a1862ba1 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb +++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb @@ -7,7 +7,7 @@ "\r\n", "Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.\r\n", "\r\n", - "[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)\r\n", + "[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/)\r\n", "\r\n", "### **Introduction**\r\n", "\r\n", @@ -439,7 +439,7 @@ "\n", "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?\n", "\n", - "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)\n", + "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/)\n", "\n", "## **Review & Self Study**\n", "\n", diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd index eeb30698..53c0cd21 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd +++ b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd @@ -14,7 +14,7 @@ output: Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. -[**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) +[**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/) ### **Introduction** @@ -315,7 +315,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) +## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/) ## **Review & Self Study** diff --git a/5-Clustering/1-Visualize/translations/README.es.md b/5-Clustering/1-Visualize/translations/README.es.md index 6a47ea30..852aa445 100644 --- a/5-Clustering/1-Visualize/translations/README.es.md +++ b/5-Clustering/1-Visualize/translations/README.es.md @@ -6,7 +6,7 @@ El agrupamiento (clustering) es un tipo de [aprendizaje no supervisado](https:// > 🎥 Haz clic en la imagen de arriba para ver el video. Mientras estudias aprendizaje automático con agrupamiento, disfruta de algunas canciones Dance Hall Nigerianas - esta es una canción muy popular del 2014 de PSquare. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27?loc=es) ### Introducción @@ -320,7 +320,7 @@ En general, para el agrupamiento, puedes usar gráficos de dispersión para most En preparación para la siguiente lección, realiza una gráfica acerca de los diverso algoritmos de agrupamiento que puedes descubrir y usar en un ambiente de producción. ¿Qué tipo de problemas trata de abordar el agrupamiento? -## [Examen porterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28?loc=es) +## [Examen porterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28?loc=es) ## Revisión y auto-estudio diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md index 3da903d4..1c9d8323 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://white-water-09ec41f0f.azurestaticapps.net/quiz/27/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/?loc=it) ### Introduzione @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/28/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/?loc=it) ## Revisione e Auto Apprendimento diff --git a/5-Clustering/1-Visualize/translations/README.ko.md b/5-Clustering/1-Visualize/translations/README.ko.md index c561bd62..8f82c2a0 100644 --- a/5-Clustering/1-Visualize/translations/README.ko.md +++ b/5-Clustering/1-Visualize/translations/README.ko.md @@ -6,7 +6,7 @@ Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정 > 🎥 영상을 보려면 이미지 클릭. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/) ### 소개 @@ -322,7 +322,7 @@ Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정 다음 강의를 준비하기 위해서, 프로덕션 환경에서 찾아서 사용할 수 있는 다양한 clustering 알고리즘을 차트로 만듭니다. clustering은 어떤 문제를 해결하려고 시도하나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/28/) ## 검토 & 자기주도 학습 diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md index c3aae1dc..e5a557eb 100644 --- a/5-Clustering/1-Visualize/translations/README.zh-cn.md +++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md @@ -6,7 +6,7 @@ > 🎥 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://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/27/) ### 介绍 @@ -326,7 +326,7 @@ 聚类试图解决什么样的问题？ -## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/) +## [课后测验](https://gentle-hill-034defd0f.1.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 ae2475fc..deb2037f 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://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/) ## Review & Self Study diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb index 7a6d69d9..88461240 100644 --- a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb +++ b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb @@ -32,7 +32,7 @@ "source": [ "## Explore K-Means clustering using R and Tidy data principles.\n", "\n", - "### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)\n", + "### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/)\n", "\n", "In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑‍🤝‍🧑), 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!\n", "\n", @@ -593,7 +593,7 @@ "\n", "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).\n", "\n", - "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)\n", + "## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/)\n", "\n", "## **Review & Self Study**\n", "\n", diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd index 2822be2a..691262b7 100644 --- a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd +++ b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd @@ -13,7 +13,7 @@ output: ## Explore K-Means clustering using R and Tidy data principles. -### [**Pre-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) +### [**Pre-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/) In this lesson, you will learn how to create clusters using the Tidymodels package and other packages in the R ecosystem (we'll call them friends 🧑‍🤝‍🧑), 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! @@ -353,7 +353,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) +## [**Post-lecture quiz**](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/) ## **Review & Self Study** diff --git a/5-Clustering/2-K-Means/translations/README.es.md b/5-Clustering/2-K-Means/translations/README.es.md index b9c696e8..3bf18c08 100644 --- a/5-Clustering/2-K-Means/translations/README.es.md +++ b/5-Clustering/2-K-Means/translations/README.es.md @@ -4,7 +4,7 @@ > 🎥 Haz clic en la imagen de arriba para ver el video: Andrew Ng explica el agrupamiento" -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29?loc=es) En esta lección, aprenderás cómo crear grupos usando Scikit-learn y el conjunto de datos de música Nigeriana que importaste anteriormente. Cubriremos los conceptos básicos de K-Medias para agrupamiento. Ten en mente que, como aprendiste en lecciones anteriores, hay muchas formas de de trabajar con grupos y el método que uses depende de tus datos. Probaremos K-medias ya que es la técnica de agrupamiento más común. ¡Comencemos! @@ -238,7 +238,7 @@ Dedica algo de tiempo a este notebook, ajustando los parámetros. ¿Puedes mejor Pista: Prueba escalar tus datos. Hay código comentado en el notebook que agrega escalado estándar para hacer que las columnas de datos se parezcan más entre sí en términos de rango. Encontrarás que mientras el puntaje de silueta disminuye el 'pliegue' en la gráfica de codo se suaviza. Esto es por qué al dejar los datos sin escalar le permite a los datos con menos variación tengan más peso. Lee un poco más de este problema [aquí](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226). -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30?loc=es) ## Revisión y auto-estudio diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md index 31f60d77..02829606 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://white-water-09ec41f0f.azurestaticapps.net/quiz/29/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/?loc=it) 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://white-water-09ec41f0f.azurestaticapps.net/quiz/30/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/?loc=it) ## Revisione e Auto Apprendimento diff --git a/5-Clustering/2-K-Means/translations/README.ko.md b/5-Clustering/2-K-Means/translations/README.ko.md index d9417d6a..d4ee91c7 100644 --- a/5-Clustering/2-K-Means/translations/README.ko.md +++ b/5-Clustering/2-K-Means/translations/README.ko.md @@ -4,7 +4,7 @@ > 🎥 영상을 보려면 이미지 클릭: Andrew Ng explains clustering -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/29/) 이 강의에서, Scikit-learn과 함께 이전에 가져온 나이지리아 음악 데이터셋으로 클러스터 제작 방식을 배울 예정입니다. Clustering을 위한 K-Means 기초를 다루게 됩니다. 참고로, 이전 강의에서 배웠던대로, 클러스터로 작업하는 여러 방식이 있고 데이터를 기반한 방식도 있습니다. 가장 일반적 clustering 기술인 K-Means을 시도해보려고 합니다. 시작해봅니다! @@ -238,7 +238,7 @@ Variance는 "the average of the squared differences from the Mean."으로 정의 힌트: 데이터를 더 키워봅니다. 가까운 범위 조건에 비슷한 데이터 열을 만들고자 추가하는 표준 스케일링 코드를 노트북에 주석으로 남겼습니다. silhouette 점수가 낮아지는 동안, elbow 그래프의 'kink'가 주름 펴지는 것을 볼 수 있습니다. 데이터를 조정하지 않고 남기면 덜 분산된 데이터가 더 많은 가중치로 나를 수 있다는 이유입니다. [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226) 이 문제를 조금 더 읽어봅니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/30/) ## 검토 & 자기주도 학습 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 7cab4496..efabf8c1 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://white-water-09ec41f0f.azurestaticapps.net/quiz/29/) +## [课前测验](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/30/) +## [课后测验](https://gentle-hill-034defd0f.1.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 ef7444cb..571d7f68 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://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/) ## Review & Self Study diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.es.md b/6-NLP/1-Introduction-to-NLP/translations/README.es.md index 620aef11..00d156f4 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.es.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.es.md @@ -2,7 +2,7 @@ Esta lección cubre una breve historia y conceptos importante del *procesamiento del lenguaje natural*, un subcampo de la *ligüística computacional*. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=es) ## Introducción @@ -150,7 +150,7 @@ Elige uno de los elementos "Detente y considera" de arriba y trata de implementa En la siguiente lección, aprenderás acerca de otros enfoques de cómo analizar el lenguaje natural y aprendizaje automático. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=es) ## Revisión y autoestudio 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 1c96d664..938b979c 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://white-water-09ec41f0f.azurestaticapps.net/quiz/31/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/?loc=it) ## 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://white-water-09ec41f0f.azurestaticapps.net/quiz/32/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/?loc=it) ## Revisione e Auto Apprendimento diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md index 719775ea..bde562c4 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md @@ -2,7 +2,7 @@ 이 강의애서 *computational linguistics* 하위인, *natural language processing*의 간단한 역사와 중요 컨셉을 다룹니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31/) ## 소개 @@ -149,7 +149,7 @@ Eliza와 같은, 대화 봇은, 사용자 입력을 유도해서 지능적으로 다음 강의에서, natural language와 머신러닝을 분석하는 여러 다른 접근 방식에 대해 배울 예정입니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32/) ## 검토 & 자기주도 학습 diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md index a2ab5cb7..70917c8d 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.pt-br.md @@ -2,7 +2,7 @@ Esta aula cobre uma breve história, bem como conceitos importantes do *processamento de linguagem natural*, uma subárea da *Linguística computacional*. -## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31?loc=ptbr) +## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/31?loc=ptbr) ## Introdução @@ -157,7 +157,7 @@ Escolha um dos elementos do "pare e considere" acima e tente implementá-lo em c Na próxima aula, você irá aprender sobre algumas outras abordagens de análise sintática de linguagem natural e de aprendizado de máquina. -## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32?loc=ptbr) +## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/32?loc=ptbr) ## Revisão & Autoestudo 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 1252f57e..b083d7a9 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://white-water-09ec41f0f.azurestaticapps.net/quiz/31/) +## [课前测验](https://gentle-hill-034defd0f.1.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。 @@ -147,7 +147,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/32/) +## [课后测验](https://gentle-hill-034defd0f.1.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 829df67b..a6ee9350 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://white-water-09ec41f0f.azurestaticapps.net/quiz/33/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/34/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/) ## Review & Self Study diff --git a/6-NLP/2-Tasks/translations/README.es.md b/6-NLP/2-Tasks/translations/README.es.md index dba482eb..02b04ff7 100644 --- a/6-NLP/2-Tasks/translations/README.es.md +++ b/6-NLP/2-Tasks/translations/README.es.md @@ -2,7 +2,7 @@ Para la mayoría de tareas de *procesamiento del lenguaje natural*, el texto a ser procesado debe ser partido en bloques, examinado y los resultados almacenados y tener referencias cruzadas con reglas y conjuntos de datos. Esta tareas, le permiten al programador obtener el _significado_, _intención_ o sólo la _frecuencia_ de los términos y palabras en un texto. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=es) Descubramos técnicas comunes usadas en el procesamiento de texto. Combinadas con el aprendizaje automático, estas técnicas te ayudan a analizar grandes cantidades de texto de forma eficiente, Antes de aplicar aprendizaje automático a estas tareas, primero entendamos los problemas encontrados por un especialista del procesamiento del lenguaje natural. @@ -203,7 +203,7 @@ Implementa el bot con la revisión de conocimiento anterior y pruébalo con un a Toma una tarea de la revisión de conocimiento previo y trata de implementarla. Prueba el bot con un amigo. ¿Pudo engañarlo? ¿Puedes hacer a tu bot más 'creíble'? -## [Examen posterior a la lectura](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34?loc=es) +## [Examen posterior a la lectura](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=es) ## Revisión y autoestudio diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md index fe70ccc0..f8f27494 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://white-water-09ec41f0f.azurestaticapps.net/quiz/33/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/?loc=it) 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://white-water-09ec41f0f.azurestaticapps.net/quiz/34/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/?loc=it) ## Revisione e Auto Apprendimento diff --git a/6-NLP/2-Tasks/translations/README.ko.md b/6-NLP/2-Tasks/translations/README.ko.md index ac6c7feb..de8e7792 100644 --- a/6-NLP/2-Tasks/translations/README.ko.md +++ b/6-NLP/2-Tasks/translations/README.ko.md @@ -2,7 +2,7 @@ 대부분 *natural language processing* 작업으로, 처리한 텍스트를 분해하고, 검사하고, 그리고 결과를 저장하거나 룰과 데이터셋을 서로 참조했습니다. 이 작업들로, 프로그래머가 _meaning_ 또는 _intent_ 또는 오직 텍스트에 있는 용어와 단어의 _frequency_ 만 끌어낼 수 있게 합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33/) 텍스트를 처리하며 사용했던 일반적인 기술을 찾아봅니다. 머신러닝에 결합된, 이 기술은 효율적으로 많은 텍스트를 분석하는데 도와줍니다. 그러나, 이 작업에 ML을 적용하기 전에, NLP 스페셜리스트가 일으킨 문제를 이해합니다. @@ -203,7 +203,7 @@ It was nice talking to you, goodbye! 이전의 지식 점검에서 작업하고 구현합니다. 친구에게 봇을 테스트합니다. 그들을 속일 수 있나요? 좀 더 '믿을 수'있게 봇을 만들 수 있나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34/) ## 검토 & 자기주도 학습 diff --git a/6-NLP/2-Tasks/translations/README.pt-br.md b/6-NLP/2-Tasks/translations/README.pt-br.md index 39ebe291..6df96eff 100644 --- a/6-NLP/2-Tasks/translations/README.pt-br.md +++ b/6-NLP/2-Tasks/translations/README.pt-br.md @@ -2,7 +2,7 @@ Para a maioria das tarefas de *processamento de linguagem natural*, o texto a ser processado precisa ser quebrado em partes e examinado, e os resultados precisam ser guardados ou cruzados com regras e data sets. Estas tarefas permitem que o programador obtenha _significado_, _intencionalidade_ ou a _frequência_ de termos e palavras em um texto. -## [Teste pré-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33?loc=ptbr) +## [Teste pré-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/33?loc=ptbr) Vamos descobrir técnicas frequentemente usadas no processamento de texto. Combinadas com aprendizado de máquina, estas técnicas ajudam você a analisar grandes quantidades de texto com eficiência. Contudo, antes de aplicar o aprendizado de máquina para estas tarefas, vamos entender os problemas enfrentados por um especialista de PLN (ou NLP). @@ -209,7 +209,7 @@ Uma possível resposta para a tarefa está [aqui](../solution/bot.py) Implemente o bot discutido acima da seção checagem de conhecimento e teste-o em amigos. O bot consegue enganá-los? Você consegue fazer seu bot mais convincente? -## [Teste pós-aula](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34?loc=ptbr) +## [Teste pós-aula](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/34?loc=ptbr) ## Revisão & Autoestudo diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index e03c48b3..5415b774 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://white-water-09ec41f0f.azurestaticapps.net/quiz/35/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/36/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/) ## Review & Self Study diff --git a/6-NLP/3-Translation-Sentiment/translations/README.es.md b/6-NLP/3-Translation-Sentiment/translations/README.es.md index 501f4e58..c86d9bef 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.es.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.es.md @@ -2,7 +2,7 @@ En las lecciones anteriores aprendiste cómo construir un bot básico usando `TextBlob`, una biblioteca que embebe aprendizaje automático tras bambalinas para realizar tareas básicas de procesamiento del lenguaje natural (NLP) tales como extracción de frases nominales. Otro desafío importante en la lingüística computacional es la _traducción_ precisa de una oración de un idioma hablado o escrito a otro. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35?loc=es) La traducción es siempre un problema difícil compuesto por el hecho que existen miles de idiomas y cada uno puede tener distintas reglas gramaticales. Un enfoque es convertir las reglas gramaticales formales para un idioma, como el Inglés, a una estructura no dependiente del idioma, y luego traducirlo al convertirlo de nuevo a otro idioma. Este enfoque significa que deberías realizar los siguientes pasos: @@ -176,7 +176,7 @@ Aquí tienes una [solución de muestra](../solution/notebook.ipynb). ¿Puedes hacer a Marvin aún mejor al extraer otras características de la entrada del usuario? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36?loc=es) ## Revisión y autoestudio diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md index 29893989..7f82d766 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://white-water-09ec41f0f.azurestaticapps.net/quiz/35/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/?loc=it) 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://white-water-09ec41f0f.azurestaticapps.net/quiz/36/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/?loc=it) ## Revisione e Auto Apprendimento diff --git a/6-NLP/3-Translation-Sentiment/translations/README.ko.md b/6-NLP/3-Translation-Sentiment/translations/README.ko.md index c9c6526e..a18dee6e 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.ko.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.ko.md @@ -2,7 +2,7 @@ 이전 강의에서 noun phrase 추출하는 기초 NLP 작업을 하기 위해 ML behind-the-scenes을 포함한 라이브러리인, `TextBlob`으로 기본적인 봇을 만드는 방식을 배웠습니다. 컴퓨터 언어학에서 다른 중요한 도전은 구두나 다른 언어로 문장을 정확하게 _translation_ 하는 것입니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/35/) 번역은 천여 개 언어와 각자 많이 다른 문법 규칙이 있다는 사실에 의해서 합쳐진 매우 어려운 문제입니다. 한 접근 방식은 영어처럼, 한 언어의 형식적인 문법 규칙을 비-언어 종속 구조로 변환하고, 다른 언어로 변환하면서 번역합니다. 이 접근 방식은 다음 단계로 진행된다는 점을 의미합니다: @@ -177,7 +177,7 @@ Darcy, as well as Elizabeth, really loved them; and they were 사용자 입력으로 다른 features를 추출해서 Marvin을 더 좋게 만들 수 있나요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/36/) ## 검토 & 자기주도 학습 diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index 393022ae..77a9537e 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://white-water-09ec41f0f.azurestaticapps.net/quiz/37/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/38/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/) ## Review & Self Study diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md index 39c6ed52..8c478465 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md @@ -6,7 +6,7 @@ En esta sección usarás las técnicas de las lecciones anteriores para hacer un - cómo calcular algunos datos nuevos basándote en las columnas existentes - cómo guardar el conjunto de datos resultante para usarlo en el desafío final -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37?loc=es) ### Introducción @@ -404,7 +404,7 @@ Ahora que has explorado el conjunto de datos, en la próxima lección filtrarás Esta lección demuestra, como vimos en lecciones anteriores, qué tan críticamente importante es entender tus datos y sus imperfecciones antes de realizar operaciones sobre ellos. Los datos basados en texto, requieren particularmente un minucioso escrutinio. Profundiza en grandes conjuntos de datos basados en texto y ve si puedes descubrir áreas que podrían presentar sesgos o sentimientos sesgados en un modelo. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38?loc=es) ## Revisión y autoestudio 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 10622683..47c36eb3 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://white-water-09ec41f0f.azurestaticapps.net/quiz/37/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/?loc=it) ### Introduzione @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/38/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/?loc=it) ## Revisione e Auto Apprendimento diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md index 59518abd..f2b2852f 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md @@ -6,7 +6,7 @@ - 이미 존재하는 열을 기반으로 일부 새로운 데이터를 계산하는 방식 - 최종 도전에서 사용하고자 결과 데이터셋을 저장하는 방식 -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/37/) ### 소개 @@ -397,7 +397,7 @@ print("Loading took " + str(round(end - start, 2)) + " seconds") 이전 강의에서 본 것처럼, 이 강의에서 작업하기 전 데이터와 약점을 이해하는 것이 얼마나 치명적이게 중요한지 보여줍니다. 특별히, 텍스트-기반 데이터는, 조심히 조사해야 합니다. 다양한 text-heavy 데이터셋을 파보고 모델에서 치우치거나 편향된 감정으로 끼워놓은 영역을 찾을 수 있는지 확인합니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/38/) ## 검토 & 자기주도 학습 diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index b8bbddd8..092ff88a 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 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://white-water-09ec41f0f.azurestaticapps.net/quiz/39/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/40/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/) ## Challenge diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md index bbd3296c..28e35d30 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md @@ -2,7 +2,7 @@ Ahora que has explorado a detalle el conjunto de datos, es momento de filtrar las columnas y luego usar técnicas de procesamiento del lenguaje natural sobre el conjunto de datos para obtener nuevos conocimientos acerca de los hoteles. -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39?loc=es) ### Filtrado y operaciones de análisis de sentimiento @@ -361,7 +361,7 @@ Para revisar, los pasos son: Cuando iniciaste, tenías un conjunto de datos con columnas y datos pero no todos ello podían ser verificados o usados. Exploraste los datos, filtraste lo que no necesitas, convertiste etiquetas en algo útil, calculaste tus propios promedios, agregaste algunas columnas de sentimiento y espero hayas aprendido cosas interesantes acerca de procesar texto natural. -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40?loc=es) ## Desafío 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 2e778000..6e62cd9a 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://white-water-09ec41f0f.azurestaticapps.net/quiz/39/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/?loc=it) ### Operazioni di Filtraggio e Analisi del Sentiment @@ -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://white-water-09ec41f0f.azurestaticapps.net/quiz/40/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/?loc=it) ## Sfida diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md index da892099..aeaa0b2d 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md @@ -2,7 +2,7 @@ 지금까지 자세히 데이터셋을 살펴보았으며, 열을 필터링하고 데이터셋으로 NLP 기술을 사용하여 호텔에 대한 새로운 시각을 얻게 될 시간입니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/39/) ### 필터링 & 감정 분석 작업 @@ -361,7 +361,7 @@ df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False) 시작했을 때, 열과 데이터로 이루어진 데이터셋이 었었지만 모두 다 확인되거나 사용되지 않았습니다. 데이터를 살펴보았으며, 필요없는 것은 필터링해서 지웠고, 유용하게 태그를 변환했고, 평균을 계산했으며, 일부 감정 열을 추가하고 기대하면서, 자연어 처리에 대한 일부 흥미로운 사실을 학습했습니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/40/) ## 도전 diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md index de17f454..e875fdc9 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://white-water-09ec41f0f.azurestaticapps.net/quiz/41/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/42/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/) ## Review & Self Study diff --git a/7-TimeSeries/1-Introduction/translations/README.es.md b/7-TimeSeries/1-Introduction/translations/README.es.md index 282dd328..04a88a99 100644 --- a/7-TimeSeries/1-Introduction/translations/README.es.md +++ b/7-TimeSeries/1-Introduction/translations/README.es.md @@ -10,7 +10,7 @@ En esta lección y la siguiente, aprenderás un poco acerca de la predicción de > 🎥 Da clic en la imagen de arriba para ver un video acerca de la predicción de series de tiempo -## [Examen previo a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41?loc=es) +## [Examen previo a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41?loc=es) Es un campo útil e interesante con valor real para el negocio, dada su aplicación directa a problemas de precio, inventario e incidentes de cadenas de suministro. Mientras que las técnicas de aprendizaje profundo han comenzado a usarse para ganar más conocimiento para mejorar el rendimiento de futuras predicciones, la predicción de series de tiempo sigue siendo un campo muy informado por técnicas de aprendizaje automático clásico. @@ -175,7 +175,7 @@ En la siguiente lección, crearás un modelo ARIMA para realizar algunas predicc Haz una lista de todas las industrias y áreas de consulta en las que puedes pensar que se beneficiarían de la predicción de series de tiempo. ¿Puedes pensar en una aplicación de estas técnicas en las artes, en la econometría, ecología, venta al menudeo, la industria, finanzas? ¿Dónde más? -## [Examen posterior a la lección](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42?loc=es) +## [Examen posterior a la lección](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42?loc=es) ## Revisión y autoestudio diff --git a/7-TimeSeries/1-Introduction/translations/README.it.md b/7-TimeSeries/1-Introduction/translations/README.it.md index 9c7830d9..5d4f49a4 100644 --- a/7-TimeSeries/1-Introduction/translations/README.it.md +++ b/7-TimeSeries/1-Introduction/translations/README.it.md @@ -10,7 +10,7 @@ In questa lezione e nella successiva si imparerà qualcosa sulla previsione dell > 🎥 Fare clic sull'immagine sopra per un video sulla previsione delle serie temporali -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/?loc=it) È un campo utile e interessante con un valore reale per il business, data la sua applicazione diretta a problemi di prezzi, inventario e problemi della catena di approvvigionamento. Mentre le tecniche di deep learning hanno iniziato a essere utilizzate per acquisire maggiori informazioni per prevedere meglio le prestazioni future, la previsione delle serie temporali rimane un campo ampiamente informato dalle tecniche classiche di ML. @@ -174,7 +174,7 @@ Nella prossima lezione, si creerà un modello ARIMA per creare alcune previsioni Fare un elenco di tutti i settori e le aree di indagine che vengono in mente che potrebbero trarre vantaggio dalla previsione delle serie temporali. Si riesce a pensare a un'applicazione di queste tecniche nelle arti? In Econometria? Ecologia? Vendita al Dettaglio? Industria? Finanza? Dove se no? -## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/?loc=it) ## Revisione e Auto Apprendimento diff --git a/7-TimeSeries/1-Introduction/translations/README.ko.md b/7-TimeSeries/1-Introduction/translations/README.ko.md index d8f04e22..a096a6e2 100644 --- a/7-TimeSeries/1-Introduction/translations/README.ko.md +++ b/7-TimeSeries/1-Introduction/translations/README.ko.md @@ -10,7 +10,7 @@ > 🎥 이미지를 눌러서 time series forecasting에 대한 비디오를 봅니다 -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/41/) 가격, 재고, 그리고 공급과 연관된 이슈에 직접 적용하게 된다면, 비지니스에 실제로 가치있는 유용하고 흥미로운 필드가 됩니다. 딥러닝 기술은 미래의 성능을 잘 예측하기 위해 더 많은 인사이트를 얻고자 사용했지만, time series forecasting은 classic ML 기술에서 지속적으로 많은 정보를 얻는 필드입니다. @@ -175,7 +175,7 @@ seasonality의 독립적으로, 1년 보다 긴 경제 침체같은 long-run cyc time series forecasting에서 얻을 수 있다고 생각할 수 있는 모든 산업과 조사 영역의 리스트를 만듭니다. 예술에 이 기술을 적용할 수 있다고 생각하나요? 경제학에서? 생태학에서? 리테일에서? 산업에서? 금융에서? 또 다른 곳은 어딘가요? -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/42/) ## 검토 & 자기주도 학습 diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index 19da5622..b421a446 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://white-water-09ec41f0f.azurestaticapps.net/quiz/43/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/44/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/) ## Review & Self Study diff --git a/7-TimeSeries/2-ARIMA/translations/README.it.md b/7-TimeSeries/2-ARIMA/translations/README.it.md index 0b08ee57..6100b0bc 100644 --- a/7-TimeSeries/2-ARIMA/translations/README.it.md +++ b/7-TimeSeries/2-ARIMA/translations/README.it.md @@ -6,7 +6,7 @@ Nella lezione precedente, si è imparato qualcosa sulla previsione delle serie t > 🎥 Fare clic sull'immagine sopra per un video: Una breve introduzione ai modelli ARIMA. L'esempio è fatto in linguaggio R, ma i concetti sono universali. -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/?loc=it) ## Introduzione @@ -383,7 +383,7 @@ Controllare l'accuratezza del modello testando il suo errore percentuale medio a Scoprire i modi per testare l'accuratezza di un modello di serie temporali. Si esamina MAPE in questa lezione, ma ci sono altri metodi che si potrebbero usare? Ricercarli e annotarli. Un documento utile può essere trovato [qui](https://otexts.com/fpp2/accuracy.html) -## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/?loc=it) ## Revisione e Auto Apprendimento diff --git a/7-TimeSeries/2-ARIMA/translations/README.ko.md b/7-TimeSeries/2-ARIMA/translations/README.ko.md index 7ed62553..030d418d 100644 --- a/7-TimeSeries/2-ARIMA/translations/README.ko.md +++ b/7-TimeSeries/2-ARIMA/translations/README.ko.md @@ -6,7 +6,7 @@ > 🎥 영상을 보려면 이미지 클릭: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/43/) ## 소개 @@ -383,7 +383,7 @@ Walk-forward 검사는 time series 모델 평가의 최적 표준이고 이 프 Time Series 모델의 정확도를 테스트할 방식을 파봅니다. 이 강의에서 MAPE을 다루지만, 사용할 다른 방식이 있나요? 조사해보고 첨언해봅니다. 도움을 받을 수 있는 문서는 [here](https://otexts.com/fpp2/accuracy.html)에서 찾을 수 있습니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/44/) ## 검토 & 자기주도 학습 diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md index 4f55894c..5fcb8719 100644 --- a/7-TimeSeries/3-SVR/README.md +++ b/7-TimeSeries/3-SVR/README.md @@ -2,7 +2,7 @@ In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data. -## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/51/) ## Introduction @@ -367,7 +367,7 @@ MAPE: 2.0572089029888656 % - Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions). - Try using different values for `timesteps` for the model to look back to make prediction. -## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/52/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/52/) ## Review & Self Study diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index 52aecd0b..2e207429 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://white-water-09ec41f0f.azurestaticapps.net/quiz/45/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/46/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/) ## Assignment [A More Realistic World](assignment.md) diff --git a/8-Reinforcement/1-QLearning/translations/README.it.md b/8-Reinforcement/1-QLearning/translations/README.it.md index 9d91cdec..a0576ea2 100644 --- a/8-Reinforcement/1-QLearning/translations/README.it.md +++ b/8-Reinforcement/1-QLearning/translations/README.it.md @@ -11,7 +11,7 @@ Usando reinforcement learning e un simulatore (il gioco), si può imparare a gio > 🎥 Fare clic sull'immagine sopra per ascoltare Dmitry discutere sul reinforcement learning -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/?loc=it) ## Prerequisiti e Configurazione @@ -315,6 +315,6 @@ Gli apprendimenti possono essere riassunti come: Nel complesso, è importante ricordare che il successo e la qualità del processo di apprendimento dipendono in modo significativo da parametri come il tasso di apprendimento, il decadimento del tasso di apprendimento e il fattore di sconto. Questi sono spesso chiamati **iperparametri**, per distinguerli dai **parametri**, che si ottimizzano durante l'allenamento (ad esempio, i coefficienti della Q-Table). Il processo per trovare i valori migliori degli iperparametri è chiamato **ottimizzazione degli iperparametri** e merita un argomento a parte. -## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/?loc=fr) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/?loc=fr) ## Incarico: [Un mondo più realistico](assignment.it.md) diff --git a/8-Reinforcement/1-QLearning/translations/README.ko.md b/8-Reinforcement/1-QLearning/translations/README.ko.md index b91313b6..b990ed5a 100644 --- a/8-Reinforcement/1-QLearning/translations/README.ko.md +++ b/8-Reinforcement/1-QLearning/translations/README.ko.md @@ -11,7 +11,7 @@ reinforcement learning과 (게임) 시뮬레이터로, 살아남고 가능한 > 🎥 Dmitry discuss Reinforcement Learning 들으려면 이미지 클릭 -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/) ## 전제조건 및 설정 @@ -315,7 +315,7 @@ print_statistics(qpolicy) 전체적으로, 학습 프로세스의 성공과 퀄리티는 학습률, 학습률 감소, 그리고 감가율처럼 파라미터에 기반하는게 상당히 중요하다는 점을 기억합니다. 훈련하면서 최적화하면 (예시로, Q-Table coefficients), **parameters**와 구별해서, 가끔 **hyperparameters**라고 불립니다. 최고의 hyperparameter 값을 찾는 프로세스는 **hyperparameter optimization**이라고 불리며, 별도의 토픽이 있을 만합니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/) ## 과제 diff --git a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md index 735d78b2..fc0479ca 100644 --- a/8-Reinforcement/1-QLearning/translations/README.zh-cn.md +++ b/8-Reinforcement/1-QLearning/translations/README.zh-cn.md @@ -11,7 +11,7 @@ > 🎥 点击上图观看 Dmitry 讨论强化学习 -## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/45/) ## 先决条件和设置 @@ -315,6 +315,6 @@ print_statistics(qpolicy) 总的来说，重要的是要记住学习过程的成功和质量在很大程度上取决于参数，例如学习率、学习率衰减和折扣因子。这些通常称为**超参数**，以区别于我们在训练期间优化的**参数**（例如，Q-Table 系数）。寻找最佳超参数值的过程称为**超参数优化**，它值得一个单独的话题来介绍。 -## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/46/) ## 作业[一个更真实的世界](assignment.zh-cn.md) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index 6331cfc6..7faea6e0 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**. https://white-water-09ec41f0f.azurestaticapps.net/ -## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/48/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/) ## Assignment: [Train a Mountain Car](assignment.md) diff --git a/8-Reinforcement/2-Gym/translations/README.it.md b/8-Reinforcement/2-Gym/translations/README.it.md index 07152028..4ed87100 100644 --- a/8-Reinforcement/2-Gym/translations/README.it.md +++ b/8-Reinforcement/2-Gym/translations/README.it.md @@ -2,7 +2,7 @@ Il problema risolto nella lezione precedente potrebbe sembrare un problema giocattolo, non propriamente applicabile a scenari di vita reale. Questo non è il caso, perché anche molti problemi del mondo reale condividono questo scenario, incluso Scacchi o Go. Sono simili, perché anche in quei casi si ha una tavolo di gioco con regole date e uno **stato discreto**. -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/?loc=it) ## Introduzione @@ -329,7 +329,7 @@ Si dovrebbe vedere qualcosa del genere: > **Compito 4**: Qui non si stava selezionando l'azione migliore per ogni passaggio, ma piuttosto campionando con la corrispondente distribuzione di probabilità. Avrebbe più senso selezionare sempre l'azione migliore, con il valore Q-Table più alto? Questo può essere fatto usando la funzione `np.argmax` per trovare il numero dell'azione corrispondente al valore della Q-Table più alto. Implementare questa strategia e vedere se migliora il bilanciamento. -## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/?loc=it) ## Compito: [addestrare un'auto di montagna](assignment.it.md) diff --git a/8-Reinforcement/2-Gym/translations/README.ko.md b/8-Reinforcement/2-Gym/translations/README.ko.md index bf2e3256..0002b2be 100644 --- a/8-Reinforcement/2-Gym/translations/README.ko.md +++ b/8-Reinforcement/2-Gym/translations/README.ko.md @@ -2,7 +2,7 @@ 이전 강의에서 풀었던 문제는 장난감 문제처럼 보일 수 있고, 실제 시나리오에서 진짜 적용되지 않습니다. 체스나 바둑을 즐기는 것을 포함한 - 시나리오에 많은 실제 문제와 공유하기 때문에, 이 케이스는 아닙니다. 주어진 룰과 **discrete state**를 보드가 가지고 있기 때문에 비슷합니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/) ## 소개 @@ -329,7 +329,7 @@ env.close() > **Task 4**: 여기에는 각 단계에서 최상의 액션을 선택하지 않고, 일치하는 확률 분포로 샘플링했습니다. 가장 높은 Q-Table 값으로, 항상 최상의 액션을 선택하면 더 합리적인가요? `np.argmax` 함수로 높은 Q-Table 값에 해당되는 액션 숫자를 찾아서 마무리할 수 있습니다. 이 전략을 구현하고 밸런스를 개선했는지 봅니다. -## [강의 후 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/) +## [강의 후 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/) ## 과제: [Train a Mountain Car](../assignment.md) diff --git a/8-Reinforcement/2-Gym/translations/README.zh-cn.md b/8-Reinforcement/2-Gym/translations/README.zh-cn.md index 19492235..783c809f 100644 --- a/8-Reinforcement/2-Gym/translations/README.zh-cn.md +++ b/8-Reinforcement/2-Gym/translations/README.zh-cn.md @@ -3,7 +3,7 @@ 我们在上一课中一直在解决的问题可能看起来像一个玩具问题，并不真正适用于现实生活场景。事实并非如此，因为许多现实世界的问题也有这种情况——包括下国际象棋或围棋。它们很相似，因为我们也有一个具有给定规则和**离散状态**的板。 https://white-water-09ec41f0f.azurestaticapps.net/ -## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/) +## [课前测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/47/) ## 介绍 @@ -330,7 +330,7 @@ env.close() > **任务 4**：这里我们不是在每一步选择最佳动作，而是用相应的概率分布进行采样。始终选择具有最高 Q-Table 值的最佳动作是否更有意义？这可以通过使用 `np.argmax` 函数找出对应于较高 Q-Table 值的动作编号来完成。实施这个策略，看看它是否能改善平衡。 -## [课后测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/) +## [课后测验](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/48/) ## 作业：[训练山地车](assignment.zh-cn.md) diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index e2eea0e6..7edc6aed 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://white-water-09ec41f0f.azurestaticapps.net/quiz/49/) +## [Pre-lecture quiz](https://gentle-hill-034defd0f.1.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://white-water-09ec41f0f.azurestaticapps.net/quiz/50/) +## [Post-lecture quiz](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/) ## Review & Self Study diff --git a/9-Real-World/1-Applications/translations/README.it.md b/9-Real-World/1-Applications/translations/README.it.md index edf84473..180460dc 100644 --- a/9-Real-World/1-Applications/translations/README.it.md +++ b/9-Real-World/1-Applications/translations/README.it.md @@ -7,7 +7,7 @@ In questo programma di studi si sono appresi molti modi per preparare i dati per Sebbene l'intelligenza artificiale abbia suscitato molto interesse nell'industria, che di solito sfrutta il deep learning, esistono ancora preziose applicazioni per i modelli classici di machine learning. Si potrebbero anche usare alcune di queste applicazioni oggi! In questa lezione, si esplorerà come otto diversi settori e campi relativi all'argomento utilizzano questi tipi di modelli per rendere le loro applicazioni più performanti, affidabili, intelligenti e preziose per gli utenti. -## [Quiz pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/?loc=it) +## [Quiz pre-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/?loc=it) ## Finanza @@ -151,7 +151,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/ Identificare un altro settore che beneficia di alcune delle tecniche apprese in questo programma di studi e scoprire come utilizza il machine learning. -## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/?loc=it) +## [Quiz post-lezione](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/?loc=it) ## Revisione e Auto Apprendimento diff --git a/9-Real-World/1-Applications/translations/README.ko.md b/9-Real-World/1-Applications/translations/README.ko.md index 265b8421..6c1a36a4 100644 --- a/9-Real-World/1-Applications/translations/README.ko.md +++ b/9-Real-World/1-Applications/translations/README.ko.md @@ -8,7 +8,7 @@ 보통 딥러닝을 활용하는, AI로 산업에 많은 관심이 모이지만, 여전히 classical 머신러닝 모델의 가치있는 애플리케이션도 존재합니다. 오늘 이 애플리케이션 일부를 사용할 수도 있습니다! 이 강의에서, 8개 다양한 산업과 subject-matter 도메인에서 이 모델 타입으로 애플리케이션의 성능, 신뢰, 지능과, 사용자 가치를 어떻게 더 높일지 탐색할 예정입니다. -## [강의 전 퀴즈](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/) +## [강의 전 퀴즈](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/49/) ## 💰 금융 @@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/ 이 커리큘럼에서 배웠던 일부 기술로 이익을 낼 다른 색터를 식별하고, ML을 어떻게 사용하는지 탐색합니다. -## [강의 후 학습](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/) +## [강의 후 학습](https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/50/) ## 검토 & 자기주도 학습 diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md index d8581817..5e58778e 100644 --- a/TRANSLATIONS.md +++ b/TRANSLATIONS.md @@ -27,7 +27,7 @@ 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://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.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://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1 becomes https://gentle-hill-034defd0f.1.azurestaticapps.net/quiz/1?loc=id **THANK YOU**