Merge pull request #609 from juliamuiruri4/quiz-app-fix

Fixed links to the lesson quizzes
pull/610/head
Lee Stott 2 years ago committed by GitHub
commit d6cbd75a5b
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GPG Key ID: 4AEE18F83AFDEB23

@ -0,0 +1,45 @@
name: Azure Static Web Apps CI/CD
on:
push:
branches:
- quiz-app-fix
pull_request:
types: [opened, synchronize, reopened, closed]
branches:
- quiz-app-fix
jobs:
build_and_deploy_job:
if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed')
runs-on: ubuntu-latest
name: Build and Deploy Job
steps:
- uses: actions/checkout@v2
with:
submodules: true
- name: Build And Deploy
id: builddeploy
uses: Azure/static-web-apps-deploy@v1
with:
azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_GENTLE_HILL_034DEFD0F }}
repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments)
action: "upload"
###### Repository/Build Configurations - These values can be configured to match your app requirements. ######
# For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig
app_location: "/quiz-app" # App source code path
api_location: "" # Api source code path - optional
output_location: "dist" # Built app content directory - optional
###### End of Repository/Build Configurations ######
close_pull_request_job:
if: github.event_name == 'pull_request' && github.event.action == 'closed'
runs-on: ubuntu-latest
name: Close Pull Request Job
steps:
- name: Close Pull Request
id: closepullrequest
uses: Azure/static-web-apps-deploy@v1
with:
azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_GENTLE_HILL_034DEFD0F }}
action: "close"

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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.

@ -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.

@ -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.

@ -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.

@ -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

@ -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。

@ -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

@ -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

@ -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。

@ -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。

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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",

@ -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**

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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",

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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]]))
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、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

@ -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

@ -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

@ -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

@ -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

@ -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

@ -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",

@ -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**

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