diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index 55381707..238e52b0 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -24,7 +24,7 @@ Welcome to this course on classical machine learning for beginners! Whether you' Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally. - **Configure your machine with these videos**. Use the following links to learn [how to install Python](https://youtu.be/CXZYvNRIAKM) in your system and [setup a text editor](https://youtu.be/EU8eayHWoZg) for development. -- **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), a programming language useful for data scientists that we use in this course. +- **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), a programming language useful for data scientists that we use in this course. - **Learn Node.js and JavaScript**. We also use JavaScript a few times in this course when building web apps, so you will need to have [node](https://nodejs.org) and [npm](https://www.npmjs.com/) installed, as well as [Visual Studio Code](https://code.visualstudio.com/) available for both Python and JavaScript development. - **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊) - **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons. @@ -139,9 +139,9 @@ Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com --- # Review & Self Study -To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). -Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) about the basics of ML. +Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) about the basics of ML. --- # Assignment diff --git a/1-Introduction/1-intro-to-ML/assignment.md b/1-Introduction/1-intro-to-ML/assignment.md index 52568cb4..dbb448c3 100644 --- a/1-Introduction/1-intro-to-ML/assignment.md +++ b/1-Introduction/1-intro-to-ML/assignment.md @@ -4,6 +4,6 @@ In this non-graded assignment, you should brush up on Python and get your environment up and running and able to run notebooks. -Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), and then get your systems setup by going through these introductory videos: +Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), and then get your systems setup by going through these introductory videos: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 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 66a4b3b7..5d1ac4dc 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.bn.md +++ b/1-Introduction/1-intro-to-ML/translations/README.bn.md @@ -26,7 +26,7 @@ MIT এর জন গাটেং মেশিন লার্নিং এর - **আপনার মেশিন কে কনফিগার করুন এই ভিডিও দেখে**. শিখার জন্য এই লিংকটি ব্যবহার করুন [কিভাবে পাইথন ইন্সটল করতে হয়](https://youtu.be/CXZYvNRIAKM) এবং [সেটআপ এ ইডিটর](https://youtu.be/EU8eayHWoZg) . -- **পাইথন শিখুন**. [পাইথন](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) এর ব্যাসিক নলেজ জানা থাকা জরুরী। এই কোর্সের প্রোগ্রামিং ল্যাঙ্গুয়েজ ডেটা সাইন্সটিস্ট এর জন্য খুবই গুরুত্বপূর্ণ। +- **পাইথন শিখুন**. [পাইথন](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) এর ব্যাসিক নলেজ জানা থাকা জরুরী। এই কোর্সের প্রোগ্রামিং ল্যাঙ্গুয়েজ ডেটা সাইন্সটিস্ট এর জন্য খুবই গুরুত্বপূর্ণ। - **Node.js এবং JavaScript শিখুন**.ওয়েব অ্যাপস তৈরির জন্য এই কোর্সে আমরা জাবাস্ক্রিপট ব্যাবহার করব। তাই, আপনার [নোড](https://nodejs.org) এবং [npm](https://www.npmjs.com/) ইন্সটল থাকতে হবে। অন্যদিকে, পাইথন এবং জাভাস্ক্রিপট ডেভেলাপমেন্টের জন্য [ভিজুয়াল স্টুডিও](https://code.visualstudio.com/) কোড এ দুটুই আছে। - **একটি গিটহাব অ্যাকাউন্ট তৈরি করুন**. যেহেতু আপনি আমাদের কে [গিটহাব](https://github.com) এ পেয়েছেন, তারমানে আপনার ইতিমধ্যেই একাউন্ট আছে। তবে যদি না থাকে, একটি একাউন্ট তৈরি করুন এবং পরে ফর্ক করে আপনার বানিয়ে নিন। (স্টার দিতে ভুলে যাবেন না,😊 ) - **ঘুরিয়ে আসেন Scikit-learn**. নিজেকে পরিচিত করুন [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) এর সাথে, মেশিন লার্নিং লাইব্রেরি সেট যা আমরা এই কোর্সে উল্লেখ করে থাকব @@ -141,9 +141,9 @@ MIT এর জন গাটেং মেশিন লার্নিং এর --- # পর্যালোচনা ও সেল্ফ স্টাডি -আপনি কিভাবে ক্লাউডে এমএল অ্যালগরিদম দিয়ে কাজ করতে পারেন সে সম্পর্কে আরও জানতে, এটি অনুসরণ করুন [লার্নিং পাথ](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)। +আপনি কিভাবে ক্লাউডে এমএল অ্যালগরিদম দিয়ে কাজ করতে পারেন সে সম্পর্কে আরও জানতে, এটি অনুসরণ করুন [লার্নিং পাথ](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)। -এম এল বেসিক জানুন [লার্নিং পাথ](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) +এম এল বেসিক জানুন [লার্নিং পাথ](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) --- # এসাইন্টমেন্ট 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 c22b6483..024dc814 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.es.md +++ b/1-Introduction/1-intro-to-ML/translations/README.es.md @@ -19,7 +19,7 @@ Antes de comenzar con este currículum, debes tener tu computadora configurada y lista para ejecutar los notebooks localmente. - **Configura tu equipo con estos videos**. Aprende más acerca de como configurar tu equipo con [estos videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Aprende Python**. También se recomienda que tengas un entendimiento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un lenguaje de programación útil para practicantes de la ciencia de datos, y que se utiliza en este curso. +- **Aprende Python**. También se recomienda que tengas un entendimiento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un lenguaje de programación útil para practicantes de la ciencia de datos, y que se utiliza en este curso. - **Aprende Node.js y JavaScript**. También usamos JavaScript unas cuantas veces en este curso cuando creamos aplicaciones web, así que necesitarás tener [node](https://nodejs.org) y [npm](https://www.npmjs.com/) instalados, así como [Visual Studio Code](https://code.visualstudio.com/) listo para el desarrollo con Python y JavaScript. - **Crea una cuenta de GitHub**. Como nos encontraste aquí en [GitHub](https://github.com), puede que ya tengas una cuenta, pero si no, créate una y después haz un fork de este curriculum para usarlo en tu computadora personal. (Siéntete libre de darnos una estrella 😊) - **Explora Scikit-learn**. Familiarízate con [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un conjunto de bibliotecas de ML que referenciamos en estas lecciones. @@ -104,9 +104,9 @@ Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.c ## Revisión y autoestudio -Para aprender más sobre como puedes trabajar con algoritmos de ML en la nube, sigue esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +Para aprender más sobre como puedes trabajar con algoritmos de ML en la nube, sigue esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). -Toma esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) sobre las bases de ML. +Toma esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre las bases de ML. ## Tarea 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 6c735191..b9c7fb31 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.fr.md +++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md @@ -18,7 +18,7 @@ Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vou Avant de commencer avec ce cours, vous aurez besoin d'un ordinateur configuré et prêt à faire tourner des notebooks (jupyter) localement. - **Configurer votre ordinateur avec ces vidéos**. Apprendre comment configurer votre ordinateur avec cette [série de vidéos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Apprendre Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un langage de programmaton utile pour les data scientist que nous utilisons tout au long de ce cours. +- **Apprendre Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un langage de programmaton utile pour les data scientist que nous utilisons tout au long de ce cours. - **Apprendre Node.js et Javascript**. Nous utilisons aussi Javascript par moment dans ce cours afin de construire des applications WEB, vous aurez donc besoin de [node](https://nodejs.org) et [npm](https://www.npmjs.com/) installé, ainsi que de [Visual Studio Code](https://code.visualstudio.com/) pour développer en Python et Javascript. - **Créer un compte GitHub**. Comme vous nous avez trouvé sur [GitHub](https://github.com), vous y avez sûrement un compte, mais si non, créez en un et répliquez ce cours afin de l'utiliser à votre grés. (N'oublier pas de nous donner une étoile aussi 😊) - **Explorer Scikit-learn**. Familiariser vous avec [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un ensemble de librairies ML que nous mentionnons dans nos leçons. @@ -102,7 +102,7 @@ Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw] ## Révision et auto-apprentissage -Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). ## Devoir 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 44d30d6c..230bddfb 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.id.md +++ b/1-Introduction/1-intro-to-ML/translations/README.id.md @@ -18,7 +18,7 @@ Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal. - **Konfigurasi komputer kamu dengan video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini. -- **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. +- **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. - **Belajar Node.js dan JavaScript**. Kita juga menggunakan JavaScript beberapa kali dalam pelajaran ini ketika membangun aplikasi web, jadi kamu perlu menginstal [node](https://nodejs.org) dan [npm](https://www.npmjs.com/), serta [Visual Studio Code](https://code.visualstudio.com/) yang tersedia untuk pengembangan Python dan JavaScript. - **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silakan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊) - **Jelajahi Scikit-learn**. Buat diri kamu familiar dengan [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), seperangkat *library* ML yang kita acu dalam pelajaran-pelajaran ini. @@ -100,7 +100,7 @@ Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https: ## Ulasan & Belajar Mandiri -Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritma ML di cloud, ikuti [Jalur Belajar](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) ini. +Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritma ML di cloud, ikuti [Jalur Belajar](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott) ini. ## Tugas 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 258f7882..37b23a0a 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.it.md +++ b/1-Introduction/1-intro-to-ML/translations/README.it.md @@ -19,7 +19,7 @@ Benvenuti in questo corso su machine learning classico per principianti! Che si Prima di iniziare con questo programma di studi, è necessario che il computer sia configurato e pronto per eseguire i notebook in locale. - **Si configuri la propria macchina con l'aiuto di questi video**. Si scopra di più su come configurare la propria macchina in questa [serie di video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso. +- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso. - **Imparare Node.js e JavaScript**. Talvolta in questo corso si usa anche JavaScript durante la creazione di app web, quindi sarà necessario disporre di [node](https://nodejs.org) e [npm](https://www.npmjs.com/) installati, oltre a [Visual Studio Code](https://code.visualstudio.com/) disponibile sia per lo sviluppo Python che JavaScript. - **Creare un account GitHub**. E' probabile che si [](https://github.com)disponga già di un account GitHub, ma in caso contrario occorre crearne uno e poi eseguire il fork di questo programma di studi per utilizzarlo autonomamente. (Sentitevi liberi di darci anche una stella 😊) - **Esplorare Scikit-learn**. Familiarizzare con Scikit-learn,[]([https://scikit-learn.org/stable/user_guide.html) un insieme di librerie ML a cui si farà riferimento in queste lezioni. @@ -101,7 +101,7 @@ Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excali ## Revisione e Auto Apprendimento -Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). ## Compito 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 f1212c8a..db0ed50d 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ja.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md @@ -17,7 +17,7 @@ このカリキュラムを始める前に、コンピュータを設定し、ノートブックをローカルで実行できるようにする必要があります。 - **こちらのビデオでマシンの設定を行ってください。** マシンの設定方法については、[これらのビデオ](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)をご覧ください。 -- **Pythonを学習する。** 本講座で使用する、データサイエンティストに有用なプログラミング言語である[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)の基本的な理解があることが望ましいです。 +- **Pythonを学習する。** 本講座で使用する、データサイエンティストに有用なプログラミング言語である[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)の基本的な理解があることが望ましいです。 - **Node.jsとJavaScriptを学習する。** このコースではウェブアプリを構築する際にJavaScriptも何度か使用しますので、[node](https://nodejs.org)と[npm](https://www.npmjs.com/)がインストールされていること、PythonとJavaScriptの両方の開発に必要な[Visual Studio Code](https://code.visualstudio.com/)が利用可能であることが必要です。 - **GitHubのアカウントを作成する。** [GitHub](https://github.com)で私たちを見つけたのですから、すでにアカウントをお持ちかもしれませんが、もしお持ちでなければ、アカウントを作成して、このカリキュラムをフォークしてご自分でお使いください。(スターをつけることもお忘れなく😊) - **Scikit-learnを探索する。** このレッスンで参照するMLライブラリのセットである[Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)に慣れ親しんでください。 @@ -98,7 +98,7 @@ AI、ML、深層学習、データサイエンスの違いについて理解し ## 振り返りと自習 -クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)に従ってください。 +クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)に従ってください。 ## 課題 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 5675daa9..eb9757d3 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ko.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ko.md @@ -18,7 +18,7 @@ 이 커리큘럼을 시작하기 전, 컴퓨터를 세팅하고 노트북을 로컬에서 실행할 수 있게 준비해야 합니다. - **이 영상으로 컴퓨터 세팅하기**. [영상 플레이리스트](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)에서 컴퓨터를 세팅하는 방법에 대하여 자세히 알아봅니다. -- **Python 배우기**. 이 코스에서 사용할 데이터 사이언티스트에게 유용한 프로그래밍 언어인 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)에 대한 기본적인 이해를 해야 좋습니다. +- **Python 배우기**. 이 코스에서 사용할 데이터 사이언티스트에게 유용한 프로그래밍 언어인 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)에 대한 기본적인 이해를 해야 좋습니다. - **Node.js 와 JavaScript 배우기**. 이 코스에서 웹앱을 빌드할 때 JavaScript를 사용하므로, [node](https://nodejs.org) 와 [npm](https://www.npmjs.com/)을 설치해야 합니다. Python 과 JavaScript의 개발환경 모두 쓸 수 있는 [Visual Studio Code](https://code.visualstudio.com/)도 있습니다. - **GitHub 계정 만들기**. [GitHub](https://github.com) 계정이 혹시 없다면, 계정을 만든 뒤에 이 커리큘럼을 포크해서 개인에 맞게 쓸 수 있습니다. (star 하셔도 됩니다 😊) - **Scikit-learn 찾아보기**. 이 강의에서 참조하고 있는 ML 라이브러리 셋인 [Scikit-learn](https://scikit-learn.org/stable/user_guide.html)을 숙지합니다. @@ -104,9 +104,9 @@ ## 리뷰 & 자기주도 학습 -클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [학습 경로](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)를 따릅니다. +클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [학습 경로](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)를 따릅니다. -ML의 기초에 대한 [학습 경로](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa)를 봅니다. +ML의 기초에 대한 [학습 경로](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott)를 봅니다. ## 과제 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 dba38e2d..9b5f94c0 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 @@ -19,7 +19,7 @@ Nossas boas vindas a este curso de machine learning clássico para iniciantes! Q Antes de iniciar este curso, você precisa ter seu computador configurado e pronto para executar notebooks localmente. - **Configure sua máquina com estes vídeos**. Use os links a seguir para aprender [como instalar o Python](https://youtu.be/CXZYvNRIAKM) em seu sistema e [configurar um editor de texto](https://youtu.be/EU8eayHWoZg) para desenvolvimento. -- **Aprenda Python**. Também é recomendável ter um conhecimento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), uma linguagem de programação útil para cientistas de dados (data scientists) que usamos neste curso. +- **Aprenda Python**. Também é recomendável ter um conhecimento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), uma linguagem de programação útil para cientistas de dados (data scientists) que usamos neste curso. - **Aprenda Node.js e JavaScript**. Também usamos JavaScript algumas vezes neste curso para criar aplicativos web, então você precisará ter [node](https://nodejs.org) e [npm](https://www.npmjs.com/) instalado, assim como o [Visual Studio Code](https://code.visualstudio.com/) disponível para desenvolvimento em Python e JavaScript. - **Crie uma conta no GitHub**. Como você nos encontrou aqui no [GitHub](https://github.com),talvez você já tenha uma conta, mas se não, crie uma e faça um fork deste curso para usar por conta própria. (Sinta-se à vontade para nos dar uma estrela também 😊). - **Explore o Scikit-learn**. Familiarize-se com o [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), um conjunto de bibliotecas de ML referenciadas nestas lições. @@ -104,9 +104,9 @@ Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excali ## Revisão e autoestudo -Para saber mais sobre como você pode trabalhar com algoritmos de ML na nuvem, siga este [Caminho de aprendizagem](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +Para saber mais sobre como você pode trabalhar com algoritmos de ML na nuvem, siga este [Caminho de aprendizagem](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). -Faça o [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) sobre os fundamentos do ML. +Faça o [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre os fundamentos do ML. ## Tarefa 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 3a415298..e7a19915 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.ru.md +++ b/1-Introduction/1-intro-to-ML/translations/README.ru.md @@ -24,7 +24,7 @@ Перед тем, как приступить к изучению этой учебной программы, вам необходимо настроить компьютер и подготовить его для работы с ноутбуками локально. - **Настройте свою машину с помощью этих видео**. Воспользуйтесь следующими ссылками, чтобы узнать [как установить Python](https://youtu.be/CXZYvNRIAKM) в вашей системе и [настроить текстовый редактор](https://youtu.be/EU8eayHWoZg) для разработки. -- **Изучите Python**. Также рекомендуется иметь базовые знания о [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), языке программирования, полезном для специалистов по данным, который мы используем в этом курсе. +- **Изучите Python**. Также рекомендуется иметь базовые знания о [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), языке программирования, полезном для специалистов по данным, который мы используем в этом курсе. - **Изучите Node.js и JavaScript**. Мы также несколько раз используем JavaScript в этом курсе при создании веб-приложений, поэтому вам потребуется установить [node](https://nodejs.org) и [npm](https://www.npmjs.com/), а также [Visual Studio Code](https://code.visualstudio.com/), доступный для разработки как на Python, так и на JavaScript. - **Создайте учетную запись GitHub**. Поскольку вы нашли нас на [GitHub](https://github.com), возможно, у вас уже есть учетная запись, но если нет, создайте ее, а затем создайте форк этой учебной программы, чтобы использовать ее самостоятельно. (Не стесняйтесь поставить звезду этому репозиторию 😊) - **Ознакомьтесь со Scikit-learn**. Ознакомьтесь со [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), набором библиотек для машинного обучения, на которые мы ссылаемся в этих уроках. @@ -139,9 +139,9 @@ --- # Обзор и самообучение -Чтобы узнать больше о том, как вы можете работать с алгоритмами машинного обучения в облаке, следуйте курсу [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa). +Чтобы узнать больше о том, как вы можете работать с алгоритмами машинного обучения в облаке, следуйте курсу [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). -Пройдите курс [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) по основам машинного обучения. +Пройдите курс [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) по основам машинного обучения. --- # Задание 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 93206b1f..79744c52 100644 --- a/1-Introduction/1-intro-to-ML/translations/README.tr.md +++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md @@ -18,7 +18,7 @@ Yeni başlayanlar için klasik makine öğrenimi üzerine olan bu kursa hoş gel Bu müfredata başlamadan önce, bilgisayarınızın yerel olarak (Jupyter) not defterlerini çalıştırmak için hazır olması gerekir. - **Makinenizi bu videolar rehberliğinde yapılandırın**. Bu [video setinde](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) makinenizi nasıl kuracağınız hakkında daha fazla bilgi edinin. -- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) programlama dili hakkında temel bilgilere sahip olmanız da önerilir. +- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) programlama dili hakkında temel bilgilere sahip olmanız da önerilir. - **Node.js ve JavaScript'i öğrenin**. Web uygulamaları oluştururken de bu kursta JavaScript'i birkaç kez kullanıyoruz, bu nedenle [node](https://nodejs.org), [npm](https://www.npmjs.com/) ve ayrıca hem Python hem de JavaScript geliştirme için kullanılabilen [Visual Studio Code](https://code.visualstudio.com/) yüklü olmalıdır. - **GitHub hesabı oluşturun**. Bizi burada [GitHub](https://github.com) üzerinde bulduğunuza göre, zaten bir hesabınız olabilir, ancak mevcut değilse, bir tane hesap oluşturun ve ardından bu müfredatı kendi başınıza kullanmak için çatallayın (fork). (Bize de yıldız vermekten çekinmeyin 😊) - **Scikit-learn'ü keşfedin**. Bu derslerde referans verdiğimiz, bir dizi ML kütüphanesinden oluşan [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) hakkında bilgi edinin. @@ -107,7 +107,7 @@ Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bi ## İnceleme ve Bireysel Çalışma -Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) izleyin. +Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott) izleyin. ## Ödev 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 977ab8ff..e919ff02 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 @@ -18,7 +18,7 @@ 在开始本课程之前，你需要设置计算机能在本地运行 Jupyter Notebooks。 - **按照这些视频里的讲解配置你的计算机**。了解有关如何在此[视频集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中设置计算机的更多信息。 -- **学习 Python**。 还建议你对 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) 有一个基本的了解。这是我们在本课程中使用的一种对数据科学家有用的编程语言。 +- **学习 Python**。 还建议你对 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) 有一个基本的了解。这是我们在本课程中使用的一种对数据科学家有用的编程语言。 - **学习 Node.js 和 JavaScript**。在本课程中，我们在构建 web 应用程序时也使用过几次 JavaScript，因此你需要有 [Node.js](https://nodejs.org) 和 [npm](https://www.npmjs.com/) 以及 [Visual Studio Code](https://code.visualstudio.com/) 用于 Python 和 JavaScript 开发。 - **创建 GitHub 帐户**。既然你在 [GitHub](https://github.com) 上找到我们，你可能已经有了一个帐户，但如果没有，请创建一个帐户，然后 fork 此课程自己使用(也给我们一颗星星吧😊) - **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我们在这些课程中引用的一组 ML 库。 @@ -100,7 +100,7 @@ ## 复习与自学 -要了解有关如何在云中使用 ML 算法的更多信息，请遵循以下[学习路径](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)。 +要了解有关如何在云中使用 ML 算法的更多信息，请遵循以下[学习路径](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)。 ## 任务 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 0902dfce..bac84df3 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 @@ -16,7 +16,7 @@ 在開始本課程之前，你需要設置計算機能在本地運行 Jupyter Notebooks。 - **按照這些視頻裏的講解配置你的計算機**。了解有關如何在此[視頻集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中設置計算機的更多信息。 -- **學習 Python**。 還建議你對 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) 有一個基本的了解。這是我們在本課程中使用的一種對數據科學家有用的編程語言。 +- **學習 Python**。 還建議你對 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) 有一個基本的了解。這是我們在本課程中使用的一種對數據科學家有用的編程語言。 - **學習 Node.js 和 JavaScript**。在本課程中，我們在構建 web 應用程序時也使用過幾次 JavaScript，因此你需要有 [Node.js](https://nodejs.org) 和 [npm](https://www.npmjs.com/) 以及 [Visual Studio Code](https://code.visualstudio.com/) 用於 Python 和 JavaScript 開發。 - **創建 GitHub 帳戶**。既然你在 [GitHub](https://github.com) 上找到我們，你可能已經有了一個帳戶，但如果沒有，請創建一個帳戶，然後 fork 此課程自己使用(也給我們一顆星星吧😊) - **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我們在這些課程中引用的一組 ML 庫。 @@ -96,7 +96,7 @@ ## 復習與自學 -要了解有關如何在雲中使用 ML 算法的更多信息，請遵循以下[學習路徑](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)。 +要了解有關如何在雲中使用 ML 算法的更多信息，請遵循以下[學習路徑](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)。 ## 任務 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.es.md b/1-Introduction/1-intro-to-ML/translations/assignment.es.md index 5241ca96..5b428135 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.es.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.es.md @@ -4,6 +4,6 @@ En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos. -Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), y luego configure sus sistemas con estos videos introductorios: +Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), y luego configure sus sistemas con estos videos introductorios: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md index 0d703d26..b8513048 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md @@ -5,6 +5,6 @@ Dans ce devoir non noté, vous devez vous familiariser avec Python et rendre votre environnement opérationnel et capable d'exécuter des notebook. -Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), puis configurez votre système en parcourant ces vidéos introductives : +Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), puis configurez votre système en parcourant ces vidéos introductives : https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.id.md b/1-Introduction/1-intro-to-ML/translations/assignment.id.md index c6ba6e4a..a22848f4 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.id.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.id.md @@ -4,6 +4,6 @@ Dalam tugas yang tidak dinilai ini, kamu akan mempelajari Python dan mempersiapkan *environment* kamu sehingga dapat digunakan untuk menjalankan *notebook*. -Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini: +Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.it.md b/1-Introduction/1-intro-to-ML/translations/assignment.it.md index b4e3cece..15c41f29 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.it.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.it.md @@ -4,6 +4,6 @@ In questo compito senza valutazione, si dovrebbe rispolverare Python e rendere il proprio ambiente attivo e funzionante, in grado di eseguire notebook. -Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) e quindi si configurino i propri sistemi seguendo questi video introduttivi: +Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) e quindi si configurino i propri sistemi seguendo questi video introduttivi: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md index 9c86969c..4c427561 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md @@ -4,6 +4,6 @@ この評価のない課題では、Pythonについて復習し、環境を稼働させてノートブックを実行できるようにする必要があります。 -この[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)を受講し、次の入門用ビデオに従ってシステムをセットアップしてください。 +この[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)を受講し、次の入門用ビデオに従ってシステムをセットアップしてください。 https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ko.md b/1-Introduction/1-intro-to-ML/translations/assignment.ko.md index 6d3cff9f..2b8e72a8 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ko.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.ko.md @@ -4,6 +4,6 @@ 이 미채점 과제에서는 파이썬(Python)을 복습하고 Python 실행 환경 설정 및 노트북(Jupyter Notebook) 실행 방법까지 숙지해 보시길 바랍니다. -다음 [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)를 이수하시고, 아래 Python 입문 강좌를 통해 Python 설치 및 실행 환경을 설정해 보세요: +다음 [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)를 이수하시고, 아래 Python 입문 강좌를 통해 Python 설치 및 실행 환경을 설정해 보세요: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md b/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md index 34732954..983d930f 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md @@ -4,6 +4,6 @@ Nesta tarefa não corrigida, você deve se aprimorar em Python e colocar seu ambiente em funcionamento e capaz de executar notebooks. -Faça o [Caminho de aprendizagem do Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), e, em seguida, faça a configuração de seus sistemas analisando estes vídeos introdutórios: +Faça o [Caminho de aprendizagem do Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), e, em seguida, faça a configuração de seus sistemas analisando estes vídeos introdutórios: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ru.md b/1-Introduction/1-intro-to-ML/translations/assignment.ru.md index bf0605b0..8a06eedb 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ru.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.ru.md @@ -4,6 +4,6 @@ Это задание не оценивается. Вы должны освежить в памяти Python и настроить свою среду, чтобы она могла запускать ноутбуки. -Воспользуйтесь этим курсом [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), а затем настройте свою систему, просмотрев эти вводные видео: +Воспользуйтесь этим курсом [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), а затем настройте свою систему, просмотрев эти вводные видео: https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.tr.md b/1-Introduction/1-intro-to-ML/translations/assignment.tr.md index 55abaf23..ed70c424 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.tr.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.tr.md @@ -4,6 +4,6 @@ Bu not-verilmeyen ödevde, Python bilgilerinizi tazelemeli, geliştirme ortamınızı çalışır duruma getirmeli ve not defterlerini çalıştırabilmelisiniz. -Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın : +Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın : https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md index fd59f691..9aa0dd28 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md @@ -4,6 +4,6 @@ 在这个不评分的作业中，你应该温习一下 Python，将 Python 环境能够运行起来，并且可以运行 notebooks。 -学习这个 [Python 学习路径](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，然后通过这些介绍性的视频将你的系统环境设置好： +学习这个 [Python 学习路径](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)，然后通过这些介绍性的视频将你的系统环境设置好： https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md index 867eeacf..fa913b28 100644 --- a/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md +++ b/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md @@ -4,6 +4,6 @@ 在這個不評分的作業中，你應該溫習一下 Python，將 Python 環境能夠運行起來，並且可以運行 notebooks。 -學習這個 [Python 學習路徑](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，然後通過這些介紹性的視頻將你的系統環境設置好： +學習這個 [Python 學習路徑](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)，然後通過這些介紹性的視頻將你的系統環境設置好： https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index 9e846cb7..166d2b6e 100644 --- a/1-Introduction/3-fairness/README.md +++ b/1-Introduction/3-fairness/README.md @@ -21,7 +21,7 @@ In this lesson, you will: As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic: -Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") @@ -169,7 +169,7 @@ The tool helps you to assesses how a model's predictions affect different groups - Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. +- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) of machine learning models in Azure Machine Learning. - Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. @@ -208,7 +208,7 @@ Explore the Fairlearn toolkit: Read about Azure Machine Learning's tools to ensure fairness: -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Assignment diff --git a/1-Introduction/3-fairness/translations/README.es.md b/1-Introduction/3-fairness/translations/README.es.md index 39afddd0..a5c39125 100644 --- a/1-Introduction/3-fairness/translations/README.es.md +++ b/1-Introduction/3-fairness/translations/README.es.md @@ -21,7 +21,7 @@ En esta lección, será capaz de: Como un prerrequisito, por favor toma la ruta de aprendizaje "Responsible AI Principles" y mira el vídeo debajo sobre el tema: -Aprende más acerca de la AI responsable siguiendo este [curso](https://docs.microsoft.com/es-es/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +Aprende más acerca de la AI responsable siguiendo este [curso](https://docs.microsoft.com/es-es/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) [![Enfonque de Microsoft para la AI responsable](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Enfoque de Microsoft para la AI responsable") @@ -168,7 +168,7 @@ La herramienta te ayuda a evaluar cómo unos modelos de predicción afectan a di - Prueba algunos [notebooks de ejemplo](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Aprende a [cómo activar evaluación de justicia](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) de los modelos de aprendizaje automático en Azure Machine Learning. +- Aprende a [cómo activar evaluación de justicia](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) de los modelos de aprendizaje automático en Azure Machine Learning. - Revisa estos [notebooks de ejemplo](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) para más escenarios de evaluaciones de justicia en Azure Machine Learning. @@ -204,7 +204,7 @@ Explorar la caja de herramientas de Fairlearn Lee acerca de las herramientas de Azure Machine Learning para asegurar justicia -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Tarea diff --git a/1-Introduction/3-fairness/translations/README.fr.md b/1-Introduction/3-fairness/translations/README.fr.md index 3313b6a0..08f654cf 100644 --- a/1-Introduction/3-fairness/translations/README.fr.md +++ b/1-Introduction/3-fairness/translations/README.fr.md @@ -21,7 +21,7 @@ Dans cette leçon, nous : En tant que prérequis, veuillez lire le guide des connaissances sur les "Principes de l'IA responsable" et regarder la vidéo sur le sujet suivant : -En apprendre plus sur l'IA responsable en suivant ce [guide des connaissances](https://docs.microsoft.com/fr-fr/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +En apprendre plus sur l'IA responsable en suivant ce [guide des connaissances](https://docs.microsoft.com/fr-fr/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) [![L'approche de Microsoft sur l'IA responsable](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") @@ -169,7 +169,7 @@ L'outil aide à évaluer comment les prédictions d'un modèle affectent différ - Essayez quelques [notebooks d'exemples](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Apprenez [comment activer les évaluations d'équités](https://docs.microsoft.com/fr-fr/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) des modèles de machine learning sur Azure Machine Learning. +- Apprenez [comment activer les évaluations d'équités](https://docs.microsoft.com/fr-fr/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) des modèles de machine learning sur Azure Machine Learning. - Jetez un coup d'oeil aux [notebooks d'exemples](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) pour plus de scénarios d'évaluation d'équités sur Azure Machine Learning. @@ -205,7 +205,7 @@ Explorer la boite à outils Fairlearn Lire sur les outils Azure Machine Learning afin d'assurer l'équité -- [Azure Machine Learning](https://docs.microsoft.com/fr-fr/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/fr-fr/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Devoir diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md index a01b3bc5..21bfd82a 100644 --- a/1-Introduction/3-fairness/translations/README.id.md +++ b/1-Introduction/3-fairness/translations/README.id.md @@ -22,7 +22,7 @@ Dalam pelajaran ini, kamu akan: Sebagai prasyarat, silakan ikuti jalur belajar "Prinsip AI yang Bertanggung Jawab" dan tonton video di bawah ini dengan topik: -Pelajari lebih lanjut tentang AI yang Bertanggung Jawab dengan mengikuti [Jalur Belajar](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) ini +Pelajari lebih lanjut tentang AI yang Bertanggung Jawab dengan mengikuti [Jalur Belajar](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) ini [![Pendekatan Microsoft untuk AI yang Bertanggung Jawab](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Pendekatan Microsoft untuk AI yang Bertanggung Jawab") @@ -170,7 +170,7 @@ Pelajaran pengantar ini tidak membahas secara mendalam mengenai detail mitigasi - Coba beberapa [sampel notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Pelajari [bagaimana cara mengaktifkan penilaian keadilan](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dari model machine learning di Azure Machine Learning. +- Pelajari [bagaimana cara mengaktifkan penilaian keadilan](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) dari model machine learning di Azure Machine Learning. - Lihat [sampel notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) ini untuk skenario penilaian keadilan yang lebih banyak di Azure Machine Learning. @@ -206,7 +206,7 @@ Jelajahi *toolkit* Fairlearn Baca mengenai *tools* Azure Machine Learning untuk memastikan keadilan -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Tugas diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md index 7176608e..9d014b84 100644 --- a/1-Introduction/3-fairness/translations/README.it.md +++ b/1-Introduction/3-fairness/translations/README.it.md @@ -21,7 +21,7 @@ In questa lezione, si dovrà: Come prerequisito, si segua il percorso di apprendimento "Principi di AI Responsabile" e si guardi il video qui sotto sull'argomento: -Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) [![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile") @@ -167,7 +167,7 @@ Lo strumento consente di valutare in che modo le previsioni di un modello influi - Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning. +- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) dei modelli di Machine Learning in Azure Machine Learning. - Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning. @@ -205,7 +205,7 @@ Si esplori il toolkit Fairlearn Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Compito diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md index 44ce4c71..cd1a0e9a 100644 --- a/1-Introduction/3-fairness/translations/README.ja.md +++ b/1-Introduction/3-fairness/translations/README.ja.md @@ -20,7 +20,7 @@ ## 前提条件 前提条件として、"Responsible AI Principles"のLearn Pathを受講し、このトピックに関する以下のビデオを視聴してください。 -こちらの[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)より、責任のあるAIについて学ぶ。 +こちらの[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)より、責任のあるAIについて学ぶ。 [![Microsoftの責任あるAIに対する取り組み](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoftの責任あるAIに対する取り組み") @@ -163,7 +163,7 @@ 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 - [サンプルノートブック](https://github.com/fairlearn/fairlearn/tree/master/notebooks)を試す。 -- Azure Machine Learningで機械学習モデルの[公平性評価を可能にする方法](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)を学ぶ。 +- Azure Machine Learningで機械学習モデルの[公平性評価を可能にする方法](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott)を学ぶ。 - Azure Machine Learningで[サンプルノートブック](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)をチェックして、公平性評価の流れを確認する。 @@ -197,7 +197,7 @@ Fairlearnのツールキットを調べてみましょう Azure Machine Learningによる、公平性を確保するためのツールについて読む -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## 課題 diff --git a/1-Introduction/3-fairness/translations/README.ko.md b/1-Introduction/3-fairness/translations/README.ko.md index 0d833e19..0feecda0 100644 --- a/1-Introduction/3-fairness/translations/README.ko.md +++ b/1-Introduction/3-fairness/translations/README.ko.md @@ -21,7 +21,7 @@ 전제 조건으로, "Responsible AI Principles" 학습 과정을 수강하고 주제에 대한 영상을 시청합니다: -[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)를 따라서 Responsible AI에 대하여 더 자세히 알아보세요 +[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)를 따라서 Responsible AI에 대하여 더 자세히 알아보세요 [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") @@ -170,7 +170,7 @@ AI와 머신러닝의 공정성을 보장하는 건 계속 복잡한 사회기 - [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks) 시도해보기. -- Azure Machine Learning에서 머신러닝 모델의 [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) 알아보기. +- Azure Machine Learning에서 머신러닝 모델의 [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) 알아보기. - Azure Machine Learning에서 더 공정한 평가 시나리오에 대하여 [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) 확인해보기. @@ -207,7 +207,7 @@ Fairlearn toolkit 탐색합니다 공정성을 보장하기 위한 Azure Machine Learning 도구에 대해 읽어봅시다 -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## 과제 diff --git a/1-Introduction/3-fairness/translations/README.pt-br.md b/1-Introduction/3-fairness/translations/README.pt-br.md index 0617e88a..cf193b09 100644 --- a/1-Introduction/3-fairness/translations/README.pt-br.md +++ b/1-Introduction/3-fairness/translations/README.pt-br.md @@ -21,7 +21,7 @@ Nesta lição, você irá: Como pré-requisito, siga o Caminho de aprendizagem "Princípios de AI responsável" e assista ao vídeo abaixo sobre o tópico: -Saiba mais sobre a AI responsável seguindo este [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +Saiba mais sobre a AI responsável seguindo este [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) [![Abordagem da Microsoft para AI responsável](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Abordagem da Microsoft para AI responsável") @@ -167,7 +167,7 @@ The tool helps you to assesses how a model's predictions affect different groups - Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. +- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) of machine learning models in Azure Machine Learning. - Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. @@ -204,7 +204,7 @@ Explore o kit de ferramentas Fairlearn Leia sobre as ferramentas do Azure Machine Learning para garantir justiça -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## Tarefa diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md index 99531065..03f30d4d 100644 --- a/1-Introduction/3-fairness/translations/README.zh-cn.md +++ b/1-Introduction/3-fairness/translations/README.zh-cn.md @@ -21,7 +21,7 @@ 作为先决条件，请选择“负责任的人工智能原则”学习路径并观看以下主题视频： -按照此[学习路径](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)了解有关负责任 AI 的更多信息 +按照此[学习路径](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)了解有关负责任 AI 的更多信息 [![微软对负责任人工智能的做法](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "微软对负责任人工智能的做法") @@ -169,7 +169,7 @@ - 尝试一些 [示例 Notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- 了解Azure机器学习中机器学习模型[如何启用公平性评估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)。 +- 了解Azure机器学习中机器学习模型[如何启用公平性评估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott)。 - 看看这些[示例 Notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)了解 Azure 机器学习中的更多公平性评估场景。 @@ -207,7 +207,7 @@ 了解 Azure 机器学习的工具以确保公平性 -- [Azure 机器学习](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure 机器学习](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## 任务 diff --git a/1-Introduction/3-fairness/translations/README.zh-tw.md b/1-Introduction/3-fairness/translations/README.zh-tw.md index 0029402e..8c8025ad 100644 --- a/1-Introduction/3-fairness/translations/README.zh-tw.md +++ b/1-Introduction/3-fairness/translations/README.zh-tw.md @@ -20,7 +20,7 @@ 作為先決條件，請選擇「負責任的人工智能原則」學習路徑並觀看以下主題視頻： -按照此[學習路徑](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)了解有關負責任 AI 的更多信息 +按照此[學習路徑](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott)了解有關負責任 AI 的更多信息 [![微軟對負責任人工智能的做法](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "微軟對負責任人工智能的做法") @@ -164,7 +164,7 @@ - 嘗試一些 [示例 Notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- 了解Azure機器學習中機器學習模型[如何啟用公平性評估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)。 +- 了解Azure機器學習中機器學習模型[如何啟用公平性評估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott)。 - 看看這些[示例 Notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)了解 Azure 機器學習中的更多公平性評估場景。 @@ -202,7 +202,7 @@ 了解 Azure 機器學習的工具以確保公平性 -- [Azure 機器學習](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure 機器學習](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) ## 任務 diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md index 171ac6ac..06aa4845 100644 --- a/1-Introduction/4-techniques-of-ML/README.md +++ b/1-Introduction/4-techniques-of-ML/README.md @@ -89,7 +89,7 @@ In the context of machine learning, model fitting refers to the accuracy of the ## Parameter tuning -Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa). +Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott). ## Prediction 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 ffcda229..75828793 100755 --- a/1-Introduction/4-techniques-of-ML/translations/README.es.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md @@ -89,7 +89,7 @@ En el contexto del machine learning, el ajuste del modelo se refiere a la precis ## Ajuste de parámetros -Una vez que haya completado su entrenamiento inicial, observe la calidad del modelo y considere mejorarlo ajustando sus 'hiperparámetros'. Lea más sobre el proceso [en la documentación](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa). +Una vez que haya completado su entrenamiento inicial, observe la calidad del modelo y considere mejorarlo ajustando sus 'hiperparámetros'. Lea más sobre el proceso [en la documentación](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott). ## Predicción 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 e9a27e25..e745955f 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.id.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md @@ -86,7 +86,7 @@ Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fun ## Parameter tuning -Setelah *training* awal selesai, amati kualitas model dan pertimbangkan untuk meningkatkannya dengan mengubah 'hyperparameter' nya. Baca lebih lanjut tentang prosesnya [di dalam dokumentasi](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa). +Setelah *training* awal selesai, amati kualitas model dan pertimbangkan untuk meningkatkannya dengan mengubah 'hyperparameter' nya. Baca lebih lanjut tentang prosesnya [di dalam dokumentasi](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott). ## Prediksi 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 5222efaa..d43f3b52 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.it.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md @@ -89,7 +89,7 @@ Nel contesto di machine learning, l'adattamento del modello si riferisce all'acc ## Sintonia dei parametri -Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa). +Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott). ## Previsione 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 5cb00144..d326dc6c 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md @@ -89,7 +89,7 @@ ## パラメータチューニング -最初のトレーニングが完了したら、モデルの質を観察して、「ハイパーパラメータ」の調整によるモデルの改善を検討しましょう。このプロセスについては [ドキュメント](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa) を読んでください。 +最初のトレーニングが完了したら、モデルの質を観察して、「ハイパーパラメータ」の調整によるモデルの改善を検討しましょう。このプロセスについては [ドキュメント](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott) を読んでください。 ## 予測 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 a57fc027..5b67f6ad 100644 --- a/1-Introduction/4-techniques-of-ML/translations/README.ko.md +++ b/1-Introduction/4-techniques-of-ML/translations/README.ko.md @@ -89,7 +89,7 @@ feature는 데이터의 측정할 수 있는 속성입니다. 많은 데이터 ## 파라미터 튜닝 -초반 훈련이 마무리 될 때, 모델의 품질을 살펴보고 'hyperparameters'를 트윅해서 개선하는 것을 고려합니다. [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa) 프로세스에 대하여 알아봅니다. +초반 훈련이 마무리 될 때, 모델의 품질을 살펴보고 'hyperparameters'를 트윅해서 개선하는 것을 고려합니다. [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott) 프로세스에 대하여 알아봅니다. ## 예측 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 683c3a31..8b3af3cc 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 @@ -89,7 +89,7 @@ No contexto do machine learning, o ajuste do modelo refere-se à precisão da fu ## Ajuste de parâmetro -Quando o treinamento inicial estiver concluído, observe a qualidade do modelo e considere melhorá-lo ajustando seus 'hiperparâmetros'. Leia mais sobre o processo [na documentação](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa). +Quando o treinamento inicial estiver concluído, observe a qualidade do modelo e considere melhorá-lo ajustando seus 'hiperparâmetros'. Leia mais sobre o processo [na documentação](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott). ## Predição 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 bc5000c1..d135b596 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 @@ -87,7 +87,7 @@ ## 参数调优 -初始训练完成后，观察模型的质量并考虑通过调整其“超参数”来改进它。[在此文档中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa)阅读有关该过程的更多信息。 +初始训练完成后，观察模型的质量并考虑通过调整其“超参数”来改进它。[在此文档中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott)阅读有关该过程的更多信息。 ## 预测 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 1f725d25..38d56d56 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 @@ -86,7 +86,7 @@ > 作者 [Jen Looper](https://twitter.com/jenlooper) ## 參數調優 -初始訓練完成後，觀察模型的質量並考慮通過調整其「超參數」來改進它。[在此文檔中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa)閱讀有關該過程的更多信息。 +初始訓練完成後，觀察模型的質量並考慮通過調整其「超參數」來改進它。[在此文檔中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-77952-leestott)閱讀有關該過程的更多信息。 ## 預測 diff --git a/1-Introduction/translations/README.zh-cn.md b/1-Introduction/translations/README.zh-cn.md index 50785646..862a5b0a 100644 --- a/1-Introduction/translations/README.zh-cn.md +++ b/1-Introduction/translations/README.zh-cn.md @@ -3,7 +3,7 @@ 课程的本章节将为您介绍机器学习领域背后的基本概念、什么是机器学习，并学习它的历史以及曾为此做出贡献的技术研究者们。让我们一起开始探索机器学习的全新世界吧！ ![globe](../images/globe.jpg) -> 图片由 Bill Oxford提供，来自 Unsplash +> 图片由 Bill Oxford 提供，来自 Unsplash ### 课程安排 diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index 263e500c..dd8fa409 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -25,13 +25,13 @@ In this lesson, you will learn how to: > 🎥 Click the image above for a video: using Python within VS Code. -1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well, to ease the setup for some users. +1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) available as well, to ease the setup for some users. Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a [virtual environment](https://docs.python.org/3/library/venv.html). -2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) for Python development. +2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) for Python development. - > Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) + > Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Install Scikit-learn**, by following [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a virtual environment. Note, if you are installing this library on a M1 Mac, there are special instructions on the page linked above. @@ -205,7 +205,7 @@ Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np. In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding. +Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) to deepen your understanding. ## Assignment diff --git a/2-Regression/1-Tools/translations/README.es.md b/2-Regression/1-Tools/translations/README.es.md index 1ce6b8b7..6178fae2 100755 --- a/2-Regression/1-Tools/translations/README.es.md +++ b/2-Regression/1-Tools/translations/README.es.md @@ -22,13 +22,13 @@ En esta lección, aprenderá a: > 🎥 Haga click en la imagen de arriba para ver un video: usando Python dentro de VS Code. -1. **Instale Python**. Asegúrese de que [Python](https://www.python.org/downloads/) esté instalado en su computadora. Utilizará Python para muchas tareas de ciencia de datos y machine learning. La mayoría de los sistemas informáticos ya incluyen una instalación de Python. También hay disponibles [paquetes de código de Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) útiles para facilitar la configuración a algunos usuarios. +1. **Instale Python**. Asegúrese de que [Python](https://www.python.org/downloads/) esté instalado en su computadora. Utilizará Python para muchas tareas de ciencia de datos y machine learning. La mayoría de los sistemas informáticos ya incluyen una instalación de Python. También hay disponibles [paquetes de código de Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) útiles para facilitar la configuración a algunos usuarios. Sin embargo algunos usos de Python requieren una versión del software, mientras otros requieren una versión diferente. Por esta razón, es útil trabajar dentro de un [entorno virtual](https://docs.python.org/3/library/venv.html). -2. **Instale Visual Studio Code**. Asegúrese de tener Visual Studio Code instalado en su computadora. Siga estas instrucciones para [instalar Visual Studio Code](https://code.visualstudio.com/) para la instalación básica. Va a utilizar Python en Visual Studio Code en este curso, por lo que es posible que desee repasar cómo [configurar Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para el desarrollo en Python. +2. **Instale Visual Studio Code**. Asegúrese de tener Visual Studio Code instalado en su computadora. Siga estas instrucciones para [instalar Visual Studio Code](https://code.visualstudio.com/) para la instalación básica. Va a utilizar Python en Visual Studio Code en este curso, por lo que es posible que desee repasar cómo [configurar Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) para el desarrollo en Python. - > Siéntase cómodo con Python trabajando con esta colección de [módulos de aprendizaje](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) + > Siéntase cómodo con Python trabajando con esta colección de [módulos de aprendizaje](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Instale Scikit-learn**, siguiendo [estas instrucciones](https://scikit-learn.org/stable/install.html). Dado que debe asegurarse de usar Python3, se recomienda que use un entorno virtual. Tenga en cuenta que si está instalando esta biblioteca en una Mac M1, hay instrucciones especiales en la página vinculada arriba. @@ -199,7 +199,7 @@ Grafique una variable diferente de este conjunto de datos. Sugerencia: edite est En este tutorial, trabajó con regresión lineal simple, en lugar de regresión lineal univariante o múltiple. Lea un poco sobre las diferencias entre estos métodos o eche un vistazo a [este video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -Lea más sobre el concepto de regresión lineal y piense que tipo de preguntas se pueden responder con esta técnica.Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para profundizar su comprensión. +Lea más sobre el concepto de regresión lineal y piense que tipo de preguntas se pueden responder con esta técnica.Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) para profundizar su comprensión. ## Asignación diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md index e9963f3b..180579f1 100644 --- a/2-Regression/1-Tools/translations/README.id.md +++ b/2-Regression/1-Tools/translations/README.id.md @@ -23,13 +23,13 @@ Dalam pelajaran ini, kamu akan belajar bagaimana untuk: > 🎥 Klik foto di atas untuk sebuah video: menggunakan Python dalam VS Code -1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Python sudah dipasang di kebanyakan sistem komputer. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna. +1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Python sudah dipasang di kebanyakan sistem komputer. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna. Beberapa penggunaan Python memerlukan satu versi perangkat lunak tersebut, sedangkan beberapa penggunaan lainnya mungkin memerlukan versi Python yang beda lagi. Oleh sebab itulah akan sangat berguna untuk bekerja dalam sebuah *[virtual environment](https://docs.python.org/3/library/venv.html)* (lingkungan virtual). -2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) untuk menggunakan Python. +2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) untuk menggunakan Python. - > Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) + > Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Pasang Scikit-learn**, dengan mengikuti [instruksi di sini](https://scikit-learn.org/stable/install.html). Karena harus dipastikan bahwa kamu sedang menggunakan Python 3, kami anjurkan kamu menggunakan sebuah *virtual environment*. Ingatlah juga bahwa jika kamu ingin memasangkan ini di sebuah M1 Mac, ada instruksi khusus dalam laman yang ditautkan di atas. @@ -201,7 +201,7 @@ Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini Dalam tutorial ini, kamu bekerja dengan sebuah model regresi linear yang sederhana daripada regresi linear univariat atau berganda. Bacalah sedikit tentang perbedaan antara metode-metode ini atau tontonlah [video ini](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef). -Bacalah lebih banyak tentang konsep regresi dan pikirkanlah tentang jenis pertanyaan apa saja yang bisa dijawab teknik ini. Cobalah [tutorial ini](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) untuk memperdalam pemahamanmu. +Bacalah lebih banyak tentang konsep regresi dan pikirkanlah tentang jenis pertanyaan apa saja yang bisa dijawab teknik ini. Cobalah [tutorial ini](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) untuk memperdalam pemahamanmu. ## Tugas diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md index 97121fad..f251de16 100644 --- a/2-Regression/1-Tools/translations/README.it.md +++ b/2-Regression/1-Tools/translations/README.it.md @@ -24,13 +24,13 @@ In questa lezione, si imparerà come: > 🎥 Fare click sull'immagine qui sopra per un video: usare Python all'interno di VS Code. -1. **Installare Python**. Assicurarsi che [Python](https://www.python.org/downloads/) sia installato nel proprio computer. Si userà Python for per molte attività di data science e machine learning. La maggior parte dei sistemi già include una installazione di Python. Ci sono anche utili [Pacchetti di Codice Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponbili, per facilitare l'installazione per alcuni utenti. +1. **Installare Python**. Assicurarsi che [Python](https://www.python.org/downloads/) sia installato nel proprio computer. Si userà Python for per molte attività di data science e machine learning. La maggior parte dei sistemi già include una installazione di Python. Ci sono anche utili [Pacchetti di Codice Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) disponbili, per facilitare l'installazione per alcuni utenti. Alcuni utilizzi di Python, tuttavia, richiedono una versione del software, laddove altri ne richiedono un'altra differente. Per questa ragione, è utile lavorare con un [ambiente virtuale](https://docs.python.org/3/library/venv.html). -2. **Installare Visual Studio Code**. Assicurarsi di avere installato Visual Studio Code sul proprio computer. Si seguano queste istruzioni per [installare Visual Studio Code](https://code.visualstudio.com/) per l'installazione basica. Si userà Python in Visual Studio Code in questo corso, quindi meglio rinfrescarsi le idee su come [configurare Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) per lo sviluppo in Python. +2. **Installare Visual Studio Code**. Assicurarsi di avere installato Visual Studio Code sul proprio computer. Si seguano queste istruzioni per [installare Visual Studio Code](https://code.visualstudio.com/) per l'installazione basica. Si userà Python in Visual Studio Code in questo corso, quindi meglio rinfrescarsi le idee su come [configurare Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) per lo sviluppo in Python. - > Si prenda confidenza con Python tramite questa collezione di [moduli di apprendimento](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) + > Si prenda confidenza con Python tramite questa collezione di [moduli di apprendimento](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Installare Scikit-learn**, seguendo [queste istruzioni](https://scikit-learn.org/stable/install.html). Visto che ci si deve assicurare di usare Python 3, ci si raccomanda di usare un ambiente virtuale. Si noti che se si installa questa libreria in un M1 Mac, ci sono istruzioni speciali nella pagina di cui al riferimento qui sopra. @@ -203,7 +203,7 @@ Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modific In questo tutorial, si è lavorato con una semplice regressione lineare, piuttosto che una regressione univariata o multipla. Ci so informi circa le differenze tra questi metodi oppure si dia uno sguardo a [questo video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -Si legga di più sul concetto di regressione e si pensi a quale tipo di domande potrebbero trovare risposta con questa tecnica. Seguire questo [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) per approfondire la propria conoscenza. +Si legga di più sul concetto di regressione e si pensi a quale tipo di domande potrebbero trovare risposta con questa tecnica. Seguire questo [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) per approfondire la propria conoscenza. ## Compito diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md index dc0207ba..8a40fbe3 100644 --- a/2-Regression/1-Tools/translations/README.ja.md +++ b/2-Regression/1-Tools/translations/README.ja.md @@ -23,14 +23,14 @@ > 🎥 上の画像をクリックするとビデオが再生されます: VisualStudioCodeでのPythonの使用方法 -1. **Pythonのインストール**: [Python](https://www.python.org/downloads/) がコンピュータにインストールされていることを確認してください。Pythonは多くのデータサイエンス、機械学習のタスクで使用します。ほとんどのコンピュータシステムにはPythonがすでにインストールされています。一部のユーザのセットアップを簡単にするために [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) を利用することもできます。 +1. **Pythonのインストール**: [Python](https://www.python.org/downloads/) がコンピュータにインストールされていることを確認してください。Pythonは多くのデータサイエンス、機械学習のタスクで使用します。ほとんどのコンピュータシステムにはPythonがすでにインストールされています。一部のユーザのセットアップを簡単にするために [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) を利用することもできます。 しかし、Pythonを使っていると時に異なるバージョンを必要とする場合があります。そのため、[仮想環境](https://docs.python.org/3/library/venv.html) を利用すると便利です。 -2. **Visual Studio Codeのインストール**: Visual Studio Codeがコンピュータにインストールされていることを確認してください。[こちらの手順](https://code.visualstudio.com/) でVisual Studio Codeをインストールしてください。このコースでは、Visual Studio CodeでPythonを使用しますので [Visual Studio Codeの設定](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) をブラッシュアップしておくといいです。 +2. **Visual Studio Codeのインストール**: Visual Studio Codeがコンピュータにインストールされていることを確認してください。[こちらの手順](https://code.visualstudio.com/) でVisual Studio Codeをインストールしてください。このコースでは、Visual Studio CodeでPythonを使用しますので [Visual Studio Codeの設定](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) をブラッシュアップしておくといいです。 - > この [学習モジュール](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) を利用して、Pythonの使い方に慣れてください。 + > この [学習モジュール](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) を利用して、Pythonの使い方に慣れてください。 3. **Scikit-learnのインストール**: [こちらの手順](https://scikit-learn.org/stable/install.html) に従ってインストールしてください。Python3の環境で実行する必要があるので、仮想環境を使用することをおすすめします。なお、このライブラリをM1のMacにインストールする場合は、上記リンク先のページに特別な説明があります。 @@ -211,7 +211,7 @@ Scikit-learnは、モデルを構築し、評価を行って実際に利用す このチュートリアルでは、単変量線形回帰や多変量線形回帰ではなく、単純線形回帰を扱いました。これらの手法の違いについて少し調べてみるか、この [ビデオ](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) を見てみましょう。 -回帰の概念について詳しく調べ、この手法でどのような質問に答えられるかを考えてみましょう。この [チュートリアル](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) で理解を深めることもできます。 +回帰の概念について詳しく調べ、この手法でどのような質問に答えられるかを考えてみましょう。この [チュートリアル](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) で理解を深めることもできます。 ## 課題 diff --git a/2-Regression/1-Tools/translations/README.ko.md b/2-Regression/1-Tools/translations/README.ko.md index bc0ac3f5..81150a70 100644 --- a/2-Regression/1-Tools/translations/README.ko.md +++ b/2-Regression/1-Tools/translations/README.ko.md @@ -23,13 +23,13 @@ > 🎥 영상 보려면 이미지 클릭: using Python within VS Code. -1. **Python 설치하기**. [Python](https://www.python.org/downloads/)이 컴퓨터에 설치되었는 지 확인합니다. 많은 데이터 사이언스와 머신러닝 작업에서 Python을 사용하게 됩니다. 대부분 컴퓨터 시스템은 이미 Python 애플리케이션을 미리 포함하고 있습니다. 사용자가 설치를 쉽게하는, 유용한 [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)이 존재합니다. +1. **Python 설치하기**. [Python](https://www.python.org/downloads/)이 컴퓨터에 설치되었는 지 확인합니다. 많은 데이터 사이언스와 머신러닝 작업에서 Python을 사용하게 됩니다. 대부분 컴퓨터 시스템은 이미 Python 애플리케이션을 미리 포함하고 있습니다. 사용자가 설치를 쉽게하는, 유용한 [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott)이 존재합니다. 그러나, 일부 Python만 사용하면, 소프트웨어의 하나의 버전만 요구하지만, 다른 건 다른 버전을 요구합니다. 이런 이유로, [virtual environment](https://docs.python.org/3/library/venv.html)에서 작업하는 것이 유용합니다. -2. **Visual Studio Code 설치하기**. 컴퓨터에 Visual Studio Code가 설치되어 있는 지 확인합니다. 기본 설치로 [install Visual Studio Code](https://code.visualstudio.com/)를 따라합니다. Visual Studio Code에서 Python을 사용하므로 Python 개발을 위한 [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa)를 살펴봅니다. +2. **Visual Studio Code 설치하기**. 컴퓨터에 Visual Studio Code가 설치되어 있는 지 확인합니다. 기본 설치로 [install Visual Studio Code](https://code.visualstudio.com/)를 따라합니다. Visual Studio Code에서 Python을 사용하므로 Python 개발을 위한 [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott)를 살펴봅니다. - > 이 [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)의 모음을 통하여 Python에 익숙해집시다. + > 이 [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott)의 모음을 통하여 Python에 익숙해집시다. 3. [these instructions](https://scikit-learn.org/stable/install.html)에 따라서, **Scikit-learn 설치하기**. Python 3을 사용하는 지 확인할 필요가 있습니다. 가상 환경으로 사용하는 것을 추천합니다. 참고로, M1 Mac에서 라이브러리를 설치하려면, 링크된 페이지에서 특별한 설치 방법을 따라합시다. @@ -206,7 +206,7 @@ Scikit-learn 사용하면 올바르게 모델을 만들고 사용하기 위해 이 튜토리얼에서, univariate 또는 multiple linear regression이 아닌 simple linear regression으로 작업했습니다. 방식의 차이를 읽어보거나, [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)를 봅니다. -regression의 개념에 대하여 더 읽고 기술로 답변할 수 있는 질문의 종류에 대하여 생각해봅니다. [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa)로 깊게 이해합니다. +regression의 개념에 대하여 더 읽고 기술로 답변할 수 있는 질문의 종류에 대하여 생각해봅니다. [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott)로 깊게 이해합니다. ## 과제 diff --git a/2-Regression/1-Tools/translations/README.pt-br.md b/2-Regression/1-Tools/translations/README.pt-br.md index 4cb68e5a..4e8fab74 100644 --- a/2-Regression/1-Tools/translations/README.pt-br.md +++ b/2-Regression/1-Tools/translations/README.pt-br.md @@ -25,13 +25,13 @@ Nesta lição, você aprenderá como: > 🎥 Clique na imagem acima para assistir o vídeo: usando Python no VS Code (vídeo em inglês). -1. **Instale Python**. Verifique se você já instalou [Python](https://www.python.org/downloads/) em seu computador. Você usará Python para muitas tarefas de _data science_ (ciência de dados) e _machine learning_. A maioria dos sistemas de computador já possui Python instalado. Existem [Pacotes de Código em Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponíveis para ajudar na instalação. +1. **Instale Python**. Verifique se você já instalou [Python](https://www.python.org/downloads/) em seu computador. Você usará Python para muitas tarefas de _data science_ (ciência de dados) e _machine learning_. A maioria dos sistemas de computador já possui Python instalado. Existem [Pacotes de Código em Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) disponíveis para ajudar na instalação. Algumas aplicações em Python exigem versões diferentes da linguagem. Portanto, será útil trabalhar com [ambiente virtual](https://docs.python.org/3/library/venv.html). -2. **Instale o Visual Studio Code**. Verifique se já existe o Visual Studio Code instalado em seu computador. Siga essas instruções para [instalar o Visual Studio Code](https://code.visualstudio.com/) com uma instalação básica. Você usará Python no Visual Studio Code neste curso e precisará [configurar o Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para isso. +2. **Instale o Visual Studio Code**. Verifique se já existe o Visual Studio Code instalado em seu computador. Siga essas instruções para [instalar o Visual Studio Code](https://code.visualstudio.com/) com uma instalação básica. Você usará Python no Visual Studio Code neste curso e precisará [configurar o Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) para isso. - > Fique mais confortável em usar Python trabalhando nessa coleção de [módulos de aprendizagem](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa). + > Fique mais confortável em usar Python trabalhando nessa coleção de [módulos de aprendizagem](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott). 3. **Instale a Scikit-learn**, seguindo [estas instruções](https://scikit-learn.org/stable/install.html). Visto que você precisa ter certeza que está usando o Python 3, é recomendável usar um ambiente virtual. Note que se você estiver usando essa biblioteca em um M1 Mac, há instruções específicas na página linkada acima. @@ -206,7 +206,7 @@ Plote uma variável diferente desse mesmo conjunto de dados. Dica: edite a linha Neste tutorial, você trabalhou com regressão linear simples, ao invés de regressão univariada ou múltipla. Leia sobre as diferença desses métodos, ou assista [esse vídeo](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef). -Leia mais sobre o conceito de regressão e pense sobre os tipos de questões que podem ser respondidas usando essa técnica. Faça esse [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para aprender mais. +Leia mais sobre o conceito de regressão e pense sobre os tipos de questões que podem ser respondidas usando essa técnica. Faça esse [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) para aprender mais. ## Tarefa diff --git a/2-Regression/1-Tools/translations/README.pt.md b/2-Regression/1-Tools/translations/README.pt.md index 948e3b32..c144ee37 100644 --- a/2-Regression/1-Tools/translations/README.pt.md +++ b/2-Regression/1-Tools/translations/README.pt.md @@ -28,14 +28,14 @@ Nesta lição, aprenderá a: > 🎥 Clique na imagem acima para um vídeo: utilizando Python dentro do Código VS. -1. **Instalar Python**. Certifique-se de que [Python](https://www.python.org/downloads/) está instalado no seu computador. Você usará Python para muitas tarefas de ciência de dados e machine learning. A maioria dos sistemas informáticos já inclui uma instalação Python. Há úteis [Python Pacotes de codificação](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponível também, para facilitar a configuração para alguns utilizadores. +1. **Instalar Python**. Certifique-se de que [Python](https://www.python.org/downloads/) está instalado no seu computador. Você usará Python para muitas tarefas de ciência de dados e machine learning. A maioria dos sistemas informáticos já inclui uma instalação Python. Há úteis [Python Pacotes de codificação](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) disponível também, para facilitar a configuração para alguns utilizadores. Alguns usos de Python, no entanto, requerem uma versão do software, enquanto outros requerem uma versão diferente. Por esta razão, é útil trabalhar dentro de um [ambiente virtual](https://docs.python.org/3/library/venv.html). 2. **Instalar código de estúdio visual**. Certifique-se de que tem o Código do Estúdio Visual instalado no seu computador. Siga estas instruções para -[instalar Código do Estúdio Visual](https://code.visualstudio.com/) para a instalação básica. Você vai usar Python em Código estúdio visual neste curso, então você pode querer relembrá-lo [configurar código de estúdio visual](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) para o desenvolvimento de Python. +[instalar Código do Estúdio Visual](https://code.visualstudio.com/) para a instalação básica. Você vai usar Python em Código estúdio visual neste curso, então você pode querer relembrá-lo [configurar código de estúdio visual](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) para o desenvolvimento de Python. -> Fique confortável com python trabalhando através desta coleção de [Aprender módulos](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) +> Fique confortável com python trabalhando através desta coleção de [Aprender módulos](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Instale Scikit-learn**, seguindo [estas instruções] (https://scikit-learn.org/stable/install.html). Uma vez que precisa de garantir que utiliza o Python 3, recomenda-se que utilize um ambiente virtual. Note que se estiver a instalar esta biblioteca num Mac M1, existem instruções especiais na página acima ligada. @@ -208,7 +208,7 @@ Defina uma variável diferente deste conjunto de dados. Dica: edite esta linha:` Neste tutorial, trabalhou com uma simples regressão linear, em vez de univariado ou regressão linear múltipla. Leia um pouco sobre as diferenças entre estes métodos, ou dê uma olhada[este vídeo](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -Leia mais sobre o conceito de regressão e pense sobre que tipo de perguntas podem ser respondidas por esta técnica. Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) para aprofundar a sua compreensão. +Leia mais sobre o conceito de regressão e pense sobre que tipo de perguntas podem ser respondidas por esta técnica. Tome este [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) para aprofundar a sua compreensão. ## Missão [Um conjunto de dados diferente](assignment.md) diff --git a/2-Regression/1-Tools/translations/README.tr.md b/2-Regression/1-Tools/translations/README.tr.md index 93cb9263..d6d50548 100644 --- a/2-Regression/1-Tools/translations/README.tr.md +++ b/2-Regression/1-Tools/translations/README.tr.md @@ -25,13 +25,13 @@ Bu derste, şunları öğreneceğiz: > 🎥 Video için yukarıdaki resme tıklayınız: Python'u VS Code içinde kullanma. -1. **Python Kurulumu**. [Python](https://www.python.org/downloads/) kurulumunun bilgisayarınızda yüklü olduğundan emin olun.Python'u birçok veri bilimi ve makine öğrenimi görevi için kullanacaksınız. Çoğu bilgisayar sistemi zaten bir Python kurulumu içerir. Şurada [Python Kodlama Paketleri](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) mevcut, bazı kullanıcılar için kurulumu daha kolay. +1. **Python Kurulumu**. [Python](https://www.python.org/downloads/) kurulumunun bilgisayarınızda yüklü olduğundan emin olun.Python'u birçok veri bilimi ve makine öğrenimi görevi için kullanacaksınız. Çoğu bilgisayar sistemi zaten bir Python kurulumu içerir. Şurada [Python Kodlama Paketleri](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-77952-leestott) mevcut, bazı kullanıcılar için kurulumu daha kolay. Ancak Python'un bazı kullanımları, yazılımın spesifik bir sürümünü gerektirir, diğerleri ise farklı bir sürüm gerektirir. Bu yüzden, [virtual environment](https://docs.python.org/3/library/venv.html) (sanal ortamlar) ile çalışmak daha kullanışlıdır. -2. **Visual Studio Code kurulumu**. Visual Studio Code'un bilgisayarınıza kurulduğundan emin olun. [Visual Studio Code kurulumu](https://code.visualstudio.com/) bu adımları takip ederek basitçe bir kurulum yapabilirsiniz. Bu kursta Python'ı Visual Studio Code'un içinde kullanacaksınız, bu yüzden nasıl yapılacağını görmek isteyebilirsiniz. Python ile geliştirme için [Visual Studio Code konfigürasyonu](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa). +2. **Visual Studio Code kurulumu**. Visual Studio Code'un bilgisayarınıza kurulduğundan emin olun. [Visual Studio Code kurulumu](https://code.visualstudio.com/) bu adımları takip ederek basitçe bir kurulum yapabilirsiniz. Bu kursta Python'ı Visual Studio Code'un içinde kullanacaksınız, bu yüzden nasıl yapılacağını görmek isteyebilirsiniz. Python ile geliştirme için [Visual Studio Code konfigürasyonu](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott). - > Bu koleksiyon üzerinde çalışarak Python ile rahatlayın. [Modülleri öğren](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) + > Bu koleksiyon üzerinde çalışarak Python ile rahatlayın. [Modülleri öğren](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 3. **Scikit-learn kurulumu**, [bu talimatları](https://scikit-learn.org/stable/install.html) takip ediniz. Python 3 kullandığınızdan emin olmanız gerektiğinden, sanal ortam kullanmanız önerilir. Not, bu kütüphaneyi bir M1 Mac'e kuruyorsanız, yukarıda bağlantısı verilen sayfada özel talimatlar var onları takip ediniz. @@ -203,7 +203,7 @@ Bu veri kümesinden farklı bir değişken çizin. İpucu: bu satırı düzenley Bu eğitimde, tek değişkenli veya çoklu doğrusal regresyon yerine basit doğrusal regresyonla çalıştınızBu yöntemler arasındaki farklar hakkında biraz bilgi edinin veya şuna bir göz atın: [bu videoya](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -Regresyon kavramı hakkında daha fazla bilgi edinin ve bu teknikle ne tür soruların yanıtlanabileceğini düşünün. Anlayışınızı derinleştirmek için bu [eğitime](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) göz atabilirsiniz. +Regresyon kavramı hakkında daha fazla bilgi edinin ve bu teknikle ne tür soruların yanıtlanabileceğini düşünün. Anlayışınızı derinleştirmek için bu [eğitime](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) göz atabilirsiniz. ## Assignment diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md index 547681ec..d7e65684 100644 --- a/2-Regression/1-Tools/translations/README.zh-cn.md +++ b/2-Regression/1-Tools/translations/README.zh-cn.md @@ -22,13 +22,13 @@ > 🎥 单击上图观看视频：在 VS Code 中使用 Python。 -1. **安装 Python**。确保你的计算机上安装了 [Python](https://www.python.org/downloads/)。你将在许多数据科学和机器学习任务中使用 Python。大多数计算机系统已经安装了 Python。也有一些有用的 [Python 编码包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-15963-cxa) 可用于简化某些用户的设置。 +1. **安装 Python**。确保你的计算机上安装了 [Python](https://www.python.org/downloads/)。你将在许多数据科学和机器学习任务中使用 Python。大多数计算机系统已经安装了 Python。也有一些有用的 [Python 编码包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-77952-leestott) 可用于简化某些用户的设置。 然而，Python 的某些用法需要一个版本的软件，而其他用法则需要另一个不同的版本。 因此，在 [虚拟环境](https://docs.python.org/3/library/venv.html) 中工作很有用。 -2. **安装 Visual Studio Code**。确保你的计算机上安装了 Visual Studio Code。按照这些说明 [安装 Visual Studio Code](https://code.visualstudio.com/) 进行基本安装。在本课程中，你将在 Visual Studio Code 中使用 Python，因此你可能想复习如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) 用于 Python 开发。 +2. **安装 Visual Studio Code**。确保你的计算机上安装了 Visual Studio Code。按照这些说明 [安装 Visual Studio Code](https://code.visualstudio.com/) 进行基本安装。在本课程中，你将在 Visual Studio Code 中使用 Python，因此你可能想复习如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) 用于 Python 开发。 - > 通过学习这一系列的 [学习模块](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) 熟悉 Python + > 通过学习这一系列的 [学习模块](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 熟悉 Python 3. **按照 [这些说明](https://scikit-learn.org/stable/install.html) 安装 Scikit learn**。由于你需要确保使用 Python3，因此建议你使用虚拟环境。注意，如果你是在 M1 Mac 上安装这个库，在上面链接的页面上有特别的说明。 @@ -200,7 +200,7 @@ Scikit-learn 使构建模型和评估它们的使用变得简单。它主要侧 在本教程中，你使用了简单线性回归，而不是单变量或多元线性回归。阅读一些关于这些方法之间差异的信息，或查看 [此视频](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -阅读有关回归概念的更多信息，并思考这种技术可以回答哪些类型的问题。用这个 [教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) 加深你的理解。 +阅读有关回归概念的更多信息，并思考这种技术可以回答哪些类型的问题。用这个 [教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) 加深你的理解。 ## 任务 diff --git a/2-Regression/1-Tools/translations/README.zh-tw.md b/2-Regression/1-Tools/translations/README.zh-tw.md index 5af1d21e..8d79c727 100644 --- a/2-Regression/1-Tools/translations/README.zh-tw.md +++ b/2-Regression/1-Tools/translations/README.zh-tw.md @@ -23,13 +23,13 @@ > 🎥 單擊上圖觀看視頻：在 VS Code 中使用 Python。 -1. **安裝 Python**。確保你的計算機上安裝了 [Python](https://www.python.org/downloads/)。你將在許多數據科學和機器學習任務中使用 Python。大多數計算機系統已經安裝了 Python。也有一些有用的 [Python 編碼包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-15963-cxa) 可用於簡化某些用戶的設置。 +1. **安裝 Python**。確保你的計算機上安裝了 [Python](https://www.python.org/downloads/)。你將在許多數據科學和機器學習任務中使用 Python。大多數計算機系統已經安裝了 Python。也有一些有用的 [Python 編碼包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-77952-leestott) 可用於簡化某些用戶的設置。 然而，Python 的某些用法需要一個版本的軟件，而其他用法則需要另一個不同的版本。 因此，在 [虛擬環境](https://docs.python.org/3/library/venv.html) 中工作很有用。 -2. **安裝 Visual Studio Code**。確保你的計算機上安裝了 Visual Studio Code。按照這些說明 [安裝 Visual Studio Code](https://code.visualstudio.com/) 進行基本安裝。在本課程中，你將在 Visual Studio Code 中使用 Python，因此你可能想復習如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) 用於 Python 開發。 +2. **安裝 Visual Studio Code**。確保你的計算機上安裝了 Visual Studio Code。按照這些說明 [安裝 Visual Studio Code](https://code.visualstudio.com/) 進行基本安裝。在本課程中，你將在 Visual Studio Code 中使用 Python，因此你可能想復習如何 [配置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-77952-leestott) 用於 Python 開發。 - > 通過學習這一系列的 [學習模塊](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) 熟悉 Python + > 通過學習這一系列的 [學習模塊](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-77952-leestott) 熟悉 Python 3. **按照 [這些說明](https://scikit-learn.org/stable/install.html) 安裝 Scikit learn**。由於你需要確保使用 Python3，因此建議你使用虛擬環境。註意，如果你是在 M1 Mac 上安裝這個庫，在上面鏈接的頁面上有特別的說明。 @@ -201,7 +201,7 @@ Scikit-learn 使構建模型和評估它們的使用變得簡單。它主要側 在本教程中，你使用了簡單線性回歸，而不是單變量或多元線性回歸。閱讀一些關於這些方法之間差異的信息，或查看 [此視頻](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) -閱讀有關回歸概念的更多信息，並思考這種技術可以回答哪些類型的問題。用這個 [教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) 加深你的理解。 +閱讀有關回歸概念的更多信息，並思考這種技術可以回答哪些類型的問題。用這個 [教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott) 加深你的理解。 ## 任務 diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index 98235a70..8427313e 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -147,7 +147,7 @@ Visualizations can also help determine the machine learning technique most appro One data visualization library that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson). -> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). +> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott). ## Exercise - experiment with Matplotlib diff --git a/2-Regression/2-Data/translations/README.es.md b/2-Regression/2-Data/translations/README.es.md index 7d5b6d20..9483f816 100644 --- a/2-Regression/2-Data/translations/README.es.md +++ b/2-Regression/2-Data/translations/README.es.md @@ -147,7 +147,7 @@ Las visualizaciones también ayudan a determinar la técnica de aprendizaje auto Una librería de visualización de datos que funciona bien en los notebooks de Jupyter es [Matplotlib](https://matplotlib.org/) (la cual también viste en la lección anterior). -> Obtén más experiencia con la visualización de datos en [estos tutoriales](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). +> Obtén más experiencia con la visualización de datos en [estos tutoriales](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott). ## Ejercicio - experimenta con Matplotlib diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md index 8db14237..d6c7de5f 100644 --- a/2-Regression/2-Data/translations/README.id.md +++ b/2-Regression/2-Data/translations/README.id.md @@ -142,7 +142,7 @@ Visualisasi juga bisa membantu menentukan teknik *machine learning* yang palingn Satu *library* visualisasi data yang bekerja dengan baik dalam sebuah *Jupyter notebook* adalah [Matplotlib](https://matplotlib.org/) (yang kamu juga lihat dalam pelajaran sebelumnya). -> Carilah pengalaman dalam memvisualisasi data dengan [tutorial-tutorial ini](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). +> Carilah pengalaman dalam memvisualisasi data dengan [tutorial-tutorial ini](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott). ## Latihan - sebuah experimen dengan Matplotlib diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md index 7fa06a3c..c0e79ef6 100644 --- a/2-Regression/2-Data/translations/README.it.md +++ b/2-Regression/2-Data/translations/README.it.md @@ -141,7 +141,7 @@ Le visualizzazioni possono anche aiutare a determinare la tecnica di machine lea Una libreria di visualizzazione dei dati che funziona bene nei notebook Jupyter è [Matplotlib](https://matplotlib.org/) (che si è visto anche nella lezione precedente). -> Per fare più esperienza con la visualizzazione dei dati si seguano [questi tutorial](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). +> Per fare più esperienza con la visualizzazione dei dati si seguano [questi tutorial](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott). ## Esercizio - sperimentare con Matplotlib diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md index b780ec40..a877bdac 100644 --- a/2-Regression/2-Data/translations/README.ja.md +++ b/2-Regression/2-Data/translations/README.ja.md @@ -146,7 +146,7 @@ Visual Studio Codeで _notebook.ipynb_ ファイルを開き、スプレッド Jupyter notebookでうまく利用できるテータ可視化ライブラリの一つに [Matplotlib](https://matplotlib.org/) があります (前のレッスンでも紹介しています)。 -> [こちらのチュートリアル](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa) でデータの可視化ついてより深く体験することができます。 +> [こちらのチュートリアル](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott) でデータの可視化ついてより深く体験することができます。 ## エクササイズ - Matplotlibの実験 diff --git a/2-Regression/2-Data/translations/README.ko.md b/2-Regression/2-Data/translations/README.ko.md index cc3ae37a..2d5a2d71 100644 --- a/2-Regression/2-Data/translations/README.ko.md +++ b/2-Regression/2-Data/translations/README.ko.md @@ -142,7 +142,7 @@ bushel 수량이 행마다 다른 것을 알았나요? bushel 단위로 가격 Jupyter notebooks에서 잘 작동하는 데이터 시각화 라이브러리는 (이전 강의에서 보았던) [Matplotlib](https://matplotlib.org/)입니다. -> [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)에서 데이터 시각화 연습을 더 해보세요. +> [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott)에서 데이터 시각화 연습을 더 해보세요. ## 연습 - Matplotlib으로 실험하기 diff --git a/2-Regression/2-Data/translations/README.pt-br.md b/2-Regression/2-Data/translations/README.pt-br.md index 0dbade75..9215bf59 100644 --- a/2-Regression/2-Data/translations/README.pt-br.md +++ b/2-Regression/2-Data/translations/README.pt-br.md @@ -148,7 +148,7 @@ As visualizações também podem ajudar a determinar a técnica de _machine lear Uma biblioteca de visualização de dados que funciona bem nos blocos de _notebooks_ é a [Matplotlib](https://matplotlib.org/) (que você também viu na lição anterior). -> Ganhe mais experiência em visualização de dados fazendo [esses tutoriais](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). +> Ganhe mais experiência em visualização de dados fazendo [esses tutoriais](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott). ## Exercício - Experimento com Matplotlib diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md index 3ee2d442..215f7411 100644 --- a/2-Regression/2-Data/translations/README.zh-cn.md +++ b/2-Regression/2-Data/translations/README.zh-cn.md @@ -142,7 +142,7 @@ 一个在 Jupyter notebooks 中运行良好的数据可视化库是 [Matplotlib](https://matplotlib.org/)（你在上一课中也看到过）。 -> 在[这些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)中获得更多数据可视化经验。 +> 在[这些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott)中获得更多数据可视化经验。 ## 练习 - 使用 Matplotlib 进行实验 diff --git a/2-Regression/2-Data/translations/README.zh-tw.md b/2-Regression/2-Data/translations/README.zh-tw.md index 2ec6a83f..d9795fd2 100644 --- a/2-Regression/2-Data/translations/README.zh-tw.md +++ b/2-Regression/2-Data/translations/README.zh-tw.md @@ -142,7 +142,7 @@ 一個在 Jupyter notebooks 中運行良好的數據可視化庫是 [Matplotlib](https://matplotlib.org/)（你在上一課中也看到過）。 -> 在[這些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)中獲得更多數據可視化經驗。 +> 在[這些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-77952-leestott)中獲得更多數據可視化經驗。 ## 練習 - 使用 Matplotlib 進行實驗 diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 8bfd426e..1c39e9a6 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -15,7 +15,7 @@ In this lesson, you will learn: - A new library for data visualization - Techniques for logistic regression -✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) +✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) ## Prerequisite Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`. 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 7770d7d9..98534c2b 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -55,7 +55,7 @@ "\n", "- Techniques for logistic regression\n", "\n", - "✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)\n", + "✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)\n", "\n", "#### **Prerequisite**\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 4f0b161f..ed6a0dc0 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -24,7 +24,7 @@ In this lesson, you will learn: - Techniques for logistic regression -✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) +✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) #### **Prerequisite** diff --git a/2-Regression/4-Logistic/translations/README.es.md b/2-Regression/4-Logistic/translations/README.es.md index 188ab653..a9640ce9 100644 --- a/2-Regression/4-Logistic/translations/README.es.md +++ b/2-Regression/4-Logistic/translations/README.es.md @@ -16,7 +16,7 @@ En esta lección, aprenderás: - Una nueva librería para visualización de datos - Técnicas para regresión logística -✅ Profundiza tu entendimiento de trabajo con este tipo de regresión en este [módulo de aprendizaje(https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) +✅ Profundiza tu entendimiento de trabajo con este tipo de regresión en este [módulo de aprendizaje(https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) ## Requisitos previos Haber trabajado con los datos de calabazas, ahora estamos suficientemente familiarizados con estos para entender que hay una categoría binaria que podemos trabajar con `Color`. diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md index cb7877b5..25ad991a 100644 --- a/2-Regression/4-Logistic/translations/README.id.md +++ b/2-Regression/4-Logistic/translations/README.id.md @@ -14,7 +14,7 @@ Dalam pelajaran ini, kamu akan belajar: - Sebuah *library* baru untuk pemvisualisasian data - Teknik-teknik untuk regresi logistik -✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) +✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) ## Prasyarat diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md index 0b60097d..17be0922 100644 --- a/2-Regression/4-Logistic/translations/README.it.md +++ b/2-Regression/4-Logistic/translations/README.it.md @@ -14,7 +14,7 @@ In questa lezione, si imparerà: - Una nuova libreria per la visualizzazione dei dati - Tecniche per la regressione logistica -✅ Con questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) si potrà approfondire la comprensione del lavoro con questo tipo di regressione +✅ Con questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) si potrà approfondire la comprensione del lavoro con questo tipo di regressione ## Prerequisito Avendo lavorato con i dati della zucca, ora si ha abbastanza familiarità con essi per rendersi conto che esiste una categoria binaria con cui è possibile lavorare: `Color` (Colore). diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md index 0b6b123d..e722dd61 100644 --- a/2-Regression/4-Logistic/translations/README.ja.md +++ b/2-Regression/4-Logistic/translations/README.ja.md @@ -13,7 +13,7 @@ - データを可視化するための新しいライブラリ - ロジスティック回帰について -✅ この[モジュール](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) では、今回のタイプのような回帰について理解を深めることができます。 +✅ この[モジュール](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) では、今回のタイプのような回帰について理解を深めることができます。 ## 前提条件 diff --git a/2-Regression/4-Logistic/translations/README.ko.md b/2-Regression/4-Logistic/translations/README.ko.md index 44ab6ecc..b8fdb228 100644 --- a/2-Regression/4-Logistic/translations/README.ko.md +++ b/2-Regression/4-Logistic/translations/README.ko.md @@ -14,7 +14,7 @@ - 데이터 시각화를 위한 새로운 라이브러리 - logistic regression 기술 -✅ [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)애서 regression의 타입에 대하여 깊게 이해해봅니다. +✅ [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott)애서 regression의 타입에 대하여 깊게 이해해봅니다. ## 필요 조건 diff --git a/2-Regression/4-Logistic/translations/README.pt-br.md b/2-Regression/4-Logistic/translations/README.pt-br.md index 70f1881b..fbf7eb51 100644 --- a/2-Regression/4-Logistic/translations/README.pt-br.md +++ b/2-Regression/4-Logistic/translations/README.pt-br.md @@ -15,7 +15,7 @@ Você irá aprender: - Uma nova biblioteca para visualização de dados - Técnicas de regressão logística -✅ Aprofunde seu conhecimento de como trabalhar com este tipo de regressão neste [módulo](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa). +✅ Aprofunde seu conhecimento de como trabalhar com este tipo de regressão neste [módulo](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott). ## Pré-requisito diff --git a/2-Regression/4-Logistic/translations/README.pt.md b/2-Regression/4-Logistic/translations/README.pt.md index 21e264db..22fb10bb 100644 --- a/2-Regression/4-Logistic/translations/README.pt.md +++ b/2-Regression/4-Logistic/translations/README.pt.md @@ -14,7 +14,7 @@ Nesta lição, aprenderá: - Uma nova biblioteca para visualização de dados - Técnicas de regressão logística -✅ aprofundar a sua compreensão de trabalhar com este tipo de regressão neste [módulo Aprender](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) +✅ aprofundar a sua compreensão de trabalhar com este tipo de regressão neste [módulo Aprender](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) ## Pré-requisito Tendo trabalhado com os dados da abóbora, estamos agora familiarizados o suficiente para perceber que há uma categoria binária com a qual podemos trabalhar:` Cor`. diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md index c23d60e4..f1aa240f 100644 --- a/2-Regression/4-Logistic/translations/README.zh-cn.md +++ b/2-Regression/4-Logistic/translations/README.zh-cn.md @@ -14,7 +14,7 @@ - 用于数据可视化的新库 - 逻辑回归技术 -✅ 在此[学习模块](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) 中加深你对使用此类回归的理解 +✅ 在此[学习模块](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) 中加深你对使用此类回归的理解 ## 前提 diff --git a/2-Regression/4-Logistic/translations/README.zh-tw.md b/2-Regression/4-Logistic/translations/README.zh-tw.md index 6eef6e5f..cd1d6bfa 100644 --- a/2-Regression/4-Logistic/translations/README.zh-tw.md +++ b/2-Regression/4-Logistic/translations/README.zh-tw.md @@ -15,7 +15,7 @@ - 用於數據可視化的新庫 - 邏輯回歸技術 -✅ 在此[學習模塊](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) 中加深你對使用此類回歸的理解 +✅ 在此[學習模塊](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-77952-leestott) 中加深你對使用此類回歸的理解 ## 前提 diff --git a/2-Regression/README.md b/2-Regression/README.md index 6750784c..87973e50 100644 --- a/2-Regression/README.md +++ b/2-Regression/README.md @@ -17,7 +17,7 @@ In this series of lessons, you'll discover the differences between linear and lo In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio Code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter. -> There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lessons diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md index ccd1733d..8a43396e 100644 --- a/2-Regression/translations/README.es.md +++ b/2-Regression/translations/README.es.md @@ -14,7 +14,7 @@ En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal En este grupo de lecciones, se preparará para comenzar las tareas de machine learning, incluida la configuración de Visual Studio Code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo. -> Existen herramientas útiles _low-code_ que pueden ayudarlo a aprender a trabajar con modelos de regresión. Pruebe [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Existen herramientas útiles _low-code_ que pueden ayudarlo a aprender a trabajar con modelos de regresión. Pruebe [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lecciones diff --git a/2-Regression/translations/README.fr.md b/2-Regression/translations/README.fr.md index 1b252f3f..d3da3e36 100644 --- a/2-Regression/translations/README.fr.md +++ b/2-Regression/translations/README.fr.md @@ -14,7 +14,7 @@ Dans cette série de leçons, vous découvrirez la différence entre la régress Dans ce groupe de leçons, vous serez préparé afin de commencer les tâches de machine learning, y compris la configuration de Visual Studio Code pour gérer les blocs-notes, l'environnement commun pour les scientifiques des données. Vous découvrirez Scikit-learn, une bibliothèque pour le machine learning, et vous construirez vos premiers modèles, en vous concentrant sur les modèles de régression dans ce chapitre. -> Il existe des outils low-code utiles qui peuvent vous aider à apprendre à travailler avec des modèles de régression. Essayez [Azure ML pour cette tâche](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Il existe des outils low-code utiles qui peuvent vous aider à apprendre à travailler avec des modèles de régression. Essayez [Azure ML pour cette tâche](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Cours diff --git a/2-Regression/translations/README.hi.md b/2-Regression/translations/README.hi.md index 06fc17f5..febefd2d 100644 --- a/2-Regression/translations/README.hi.md +++ b/2-Regression/translations/README.hi.md @@ -17,7 +17,7 @@ पाठों के इस समूह में, आप मशीन लर्निंग सीखने के कार्यों को शुरू करने के लिए तैयार होंगे, जिसमें नोटबुक को प्रबंधित करने के लिए विजुअल स्टूडियो कोड को कॉन्फ़िगर करना, डेटा वैज्ञानिकों के लिए सामान्य वातावरण शामिल है। आप मशीन लर्निंग के लिए एक लाइब्रेरी स्किकिट-लर्न की खोज करेंगे, और आप इस अध्याय में रिग्रेशन मॉडल पर ध्यान केंद्रित करते हुए अपना पहला मॉडल बनाएंगे। ->ये उपयोगी निम्न-कोड उपकरण हैं जो आपको रिग्रेशन मॉडल के साथ काम करने के बारे में जानने में मदद कर सकते हैं.इस्तेमाल करे [इस कार्य के लिए अज़ूरे एमएल](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +>ये उपयोगी निम्न-कोड उपकरण हैं जो आपको रिग्रेशन मॉडल के साथ काम करने के बारे में जानने में मदद कर सकते हैं.इस्तेमाल करे [इस कार्य के लिए अज़ूरे एमएल](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### पाठ diff --git a/2-Regression/translations/README.id.md b/2-Regression/translations/README.id.md index da1fa193..8c90b01e 100644 --- a/2-Regression/translations/README.id.md +++ b/2-Regression/translations/README.id.md @@ -14,7 +14,7 @@ Dalam seri pelajaran ini, kamu akan menemukan perbedaan antara regresi linear da Selain itu, kamu akan disiapkan untuk mulai mengerjakan tugas *machine learning*, termasuk mengkonfigurasi Visual Studio Code untuk mengelola *notebook*, lingkungan wajar untuk *data scientist*. Kamu akan menemukan Scikit-learn, sebuah *library* untuk *machine learning*, dan kamu akan membangun model pertamamu dengan memfokus pada model regresi dalam bab ini. -> Ada alat-alat *low-code* yang dapat membantumu belajar tentang bekerja dengan model regresi. Cobalah [Azure ML untuk tugas ini](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Ada alat-alat *low-code* yang dapat membantumu belajar tentang bekerja dengan model regresi. Cobalah [Azure ML untuk tugas ini](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ### Pelajaran diff --git a/2-Regression/translations/README.it.md b/2-Regression/translations/README.it.md index c6e957f9..2252ca03 100644 --- a/2-Regression/translations/README.it.md +++ b/2-Regression/translations/README.it.md @@ -15,7 +15,7 @@ In questa serie di lezioni si scoprirà la differenza tra regressione lineare e In questo gruppo di lezioni si imposterà una configurazione per iniziare le attività di machine learning, inclusa la configurazione di Visual Studio Code per gestire i notebook, l'ambiente comune per i data scientist. Si scoprirà Scikit-learn, una libreria per machine learning, e si creeranno i primi modelli, concentrandosi in questo capitolo sui modelli di Regressione. -> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure Machine Learning per questa attività](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure Machine Learning per questa attività](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lezioni diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md index 50d8294b..a36ab023 100644 --- a/2-Regression/translations/README.ja.md +++ b/2-Regression/translations/README.ja.md @@ -13,7 +13,7 @@ データサイエンティストの共通開発環境であるノートブックを管理するためのVisual Studio Codeの構成や機械学習のタスクを開始するための準備を行います。また、機械学習用のライブラリであるScikit-learnを利用し最初のモデルを構築します。この章では回帰モデルに焦点を当てます。 -> 回帰モデルを学習するのに役立つローコードツールがあります。ぜひ[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)を使ってみてください。 +> 回帰モデルを学習するのに役立つローコードツールがあります。ぜひ[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)を使ってみてください。 ### レッスン diff --git a/2-Regression/translations/README.ko.md b/2-Regression/translations/README.ko.md index 81a16ec3..ee57ae83 100644 --- a/2-Regression/translations/README.ko.md +++ b/2-Regression/translations/README.ko.md @@ -15,7 +15,7 @@ 이 강의의 그룹에서, 데이터 사이언티스트를 위한 일반적 환경의, 노트북을 관리할 Visual Studio code 구성을 포함해서, 머신러닝 작업을 시작하도록 맞춥니다. 머신러닝을 위한 라이브러리인, Scikit-learn을 찾고, 이 챕터의 Regression 모델에 초점을 맞추어, 첫 모델을 만들 예정입니다. -> Regression 모델을 작업할 때 배울 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)를 시도해보세요. +> Regression 모델을 작업할 때 배울 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)를 시도해보세요. ### Lessons diff --git a/2-Regression/translations/README.pt-br.md b/2-Regression/translations/README.pt-br.md index 22e28fc4..295cc049 100644 --- a/2-Regression/translations/README.pt-br.md +++ b/2-Regression/translations/README.pt-br.md @@ -15,7 +15,7 @@ Nesta série de lições, você descobrirá a diferença entre regressão linear Neste grupo de lições, te prepararemos para começar tarefas de _machine learning_, incluindo configuração do Visual Studio Code para gerenciar _notebooks_, o ambiente comum para _data scientists_ (cientistas de dados). Você descobrirá a Scikit-learn, uma biblioteca para _machine learning_, e construirá seus primeiros modelos, focando em modelos de regressão neste capítulo. -> Existem ferramentas _low-code_ que podem ajudar a aprender como trabalhar com modelos de regressão. Use a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Existem ferramentas _low-code_ que podem ajudar a aprender como trabalhar com modelos de regressão. Use a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ### Lições diff --git a/2-Regression/translations/README.pt.md b/2-Regression/translations/README.pt.md index 4921f16f..6a13c908 100644 --- a/2-Regression/translations/README.pt.md +++ b/2-Regression/translations/README.pt.md @@ -19,7 +19,7 @@ Nesta série de lições, você vai descobrir a diferença entre regressão log Neste grupo de lições, você será configurado para iniciar tarefas de machine learning, incluindo configurar o Código do Estúdio Visual para gerir cadernos, o ambiente comum para cientistas de dados. Você vai descobrir Scikit-learn, uma biblioteca para machine learning, e você vai construir seus primeiros modelos, focando-se em modelos de Regressão neste capítulo. > Existem ferramentas de baixo código úteis que podem ajudá-lo a aprender sobre trabalhar com modelos de regressão. Tente -[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lessons diff --git a/2-Regression/translations/README.ru.md b/2-Regression/translations/README.ru.md index 1d63d6ad..3e89a1fa 100644 --- a/2-Regression/translations/README.ru.md +++ b/2-Regression/translations/README.ru.md @@ -17,7 +17,7 @@ В этой группе уроков вы будете подготовлены, чтобы приступить к задачам машинного обучения, включая настройку Visual Studio Code для управления записными книжками, распространенным иструментом среди специалистов по данным. Вы откроете для себя scikit-learn, библиотеку для машинного обучения, и создадите свои первые модели, фокусируясь на регрессии в этой главе. -> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях регрессии. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях регрессии. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ### Уроки diff --git a/2-Regression/translations/README.tr.md b/2-Regression/translations/README.tr.md index 583aee13..8d4b043e 100644 --- a/2-Regression/translations/README.tr.md +++ b/2-Regression/translations/README.tr.md @@ -17,7 +17,7 @@ Bu dersler dizisinde, lineer ve lojistik regresyon arasındaki farkları ve ne z Bu ders grubunda, veri bilimcileri için ortak ortam olan not defterlerini yönetmek için Visual Studio Code'un yapılandırılması dahil olmak üzere makine öğrenimi görevlerine başlamak için hazırlanacaksınız. -> Regresyon modelleriyle çalışma hakkında bilgi edinmenize yardımcı olabilecek kullanışlı low-code (az kodlamalı) araçlar vardır. Bunu deneyin. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Regresyon modelleriyle çalışma hakkında bilgi edinmenize yardımcı olabilecek kullanışlı low-code (az kodlamalı) araçlar vardır. Bunu deneyin. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Dersler diff --git a/2-Regression/translations/README.zh-cn.md b/2-Regression/translations/README.zh-cn.md index f7c511e6..bebe0c93 100644 --- a/2-Regression/translations/README.zh-cn.md +++ b/2-Regression/translations/README.zh-cn.md @@ -14,7 +14,7 @@ 在这组课程中，你会准备好包括为管理笔记而设置VS Code、配置数据科学家常用的环境等机器学习的初始任务。你会开始上手Scikit-learn学习项目（一个机器学习的百科），并且你会以回归模型为主构建起你的第一种机器学习模型 -> 这里有一些代码难度较低但很有用的工具可以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> 这里有一些代码难度较低但很有用的工具可以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lessons diff --git a/2-Regression/translations/README.zh-tw.md b/2-Regression/translations/README.zh-tw.md index 5da4eb94..47ef1f34 100644 --- a/2-Regression/translations/README.zh-tw.md +++ b/2-Regression/translations/README.zh-tw.md @@ -15,7 +15,7 @@ 在這組課程中，你會準備好包括為管理筆記而設置VS Code、配置數據科學家常用的環境等機器學習的初始任務。你會開始上手Scikit-learn學習項目（一個機器學習的百科），並且你會以回歸模型為主構建起你的第一種機器學習模型 -> 這裏有一些代碼難度較低但很有用的工具可以幫助你學習使用回歸模型。 試一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> 這裏有一些代碼難度較低但很有用的工具可以幫助你學習使用回歸模型。 試一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ### Lessons diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index cc9790ee..29278660 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -27,7 +27,7 @@ There are many questions you need to ask: - **What technology was used to train the model?** The chosen technology may influence the tooling you need to use. - **Using TensorFlow.** If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/). - **Using PyTorch.** If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model. - - **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application. + - **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application. You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context. @@ -37,7 +37,7 @@ For our purposes, since we have been working with Python-based notebooks, let's For this task, you need two tools: Flask and Pickle, both of which run on Python. -✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask. +✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) to practice building with Flask. ✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`. 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 c98a8fcf..a2ec59a5 100644 --- a/3-Web-App/1-Web-App/translations/README.es.md +++ b/3-Web-App/1-Web-App/translations/README.es.md @@ -27,7 +27,7 @@ Hay muchas preguntas que necesitas realizar: - **¿Qué tecnología se usó para entrenar al modelo?** La tecnología elegida puede influir en las herramientas que necesitas utilizar. - **Uso de TensorFlow.** Si estás entrenando un modelo usando TensorFlow, por ejemplo, ese ecosistema proporciona la capacidad de convertir un modelo de TensorFlow para su uso en una aplicación web usando [TensorFlow.js](https://www.tensorflow.org/js/). - **Uso de PyTorch.** Si estás construyendo un modelo usando una librería como [PyTorch](https://pytorch.org/), tienes la opción de exportarlo en formato [ONNX](https://onnx.ai/) (Open Neural Network Exchange) para usarlo en aplicaciones web de javascript que puedan usar el entorno de ejecución [Onnx Runtime](https://www.onnxruntime.ai/). Esta opción será explorada en una futura lección para un modelo entrenado Scikit-learn. - - **Uso de Lobe.ai o Azure Custom Vision.** Si estás usando un sistema de aprendizaje automático SaaS (Software as a Service) como lo es [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) para entrenar un modelo, este tipo de software proporciona formas de exportar el modelo a diversas plataformas, incluyendo el construir una API a medida para que esta sea consultada en la nube por tu aplicación en línea. + - **Uso de Lobe.ai o Azure Custom Vision.** Si estás usando un sistema de aprendizaje automático SaaS (Software as a Service) como lo es [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) para entrenar un modelo, este tipo de software proporciona formas de exportar el modelo a diversas plataformas, incluyendo el construir una API a medida para que esta sea consultada en la nube por tu aplicación en línea. También tienes la oportunidad de construir una plicación web completamente en Flask que será capaz de entrenar el propio modelo en un navegador web. Esto también puede ser realizado usando TensorFlow.js en un contexto JavaScript. @@ -37,7 +37,7 @@ Para nuestros propósitos, ya que hemos estado trabajando con notebooks basados Para esta tarea, necesitas dos herramientas: Flask y Pickle, ambos corren en Python. -✅ ¿Qué es [Flask](https://palletsprojects.com/p/flask/)? Definido como un 'micro-framework' por sus creadores, Flask proporciona las características básicas de los frameworks web usando Python y un motor de plantillas para construir páginas web. Da un vistazo a [este módulo de aprendizaje](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para practicar construir con Flask. +✅ ¿Qué es [Flask](https://palletsprojects.com/p/flask/)? Definido como un 'micro-framework' por sus creadores, Flask proporciona las características básicas de los frameworks web usando Python y un motor de plantillas para construir páginas web. Da un vistazo a [este módulo de aprendizaje](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para practicar construir con Flask. ✅ ¿Qué es [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 es un módulo de Python que serializa y deserializa estructura de objetos Python. Cuando conviertes un modelo en 'pickle', serializas o aplanas su estructura para su uso en la web. Sé cuidadoso: Pickle no es intrínsecamente seguro, por lo que debes ser cuidadoso si solicitaste hacer 'un-pickle' en un archivo. Un archivo hecho pickle tiene el sufijo `.pkl`. 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 a2c9c7a8..e41159ba 100644 --- a/3-Web-App/1-Web-App/translations/README.it.md +++ b/3-Web-App/1-Web-App/translations/README.it.md @@ -27,7 +27,7 @@ Ci sono molte domande da porsi: - **Quale tecnologia è stata utilizzata per addestrare il modello?** La tecnologia scelta può influenzare gli strumenti che è necessario utilizzare. - **Utilizzare** TensorFlow. Se si sta addestrando un modello utilizzando TensorFlow, ad esempio, tale ecosistema offre la possibilità di convertire un modello TensorFlow per l'utilizzo in un'app Web utilizzando [TensorFlow.js](https://www.tensorflow.org/js/). - **Utilizzare PyTorch**. Se si sta costruendo un modello utilizzando una libreria come PyTorch[,](https://pytorch.org/) si ha la possibilità di esportarlo in formato [ONNX](https://onnx.ai/) ( Open Neural Network Exchange) per l'utilizzo in app Web JavaScript che possono utilizzare il [motore di esecuzione Onnx](https://www.onnxruntime.ai/). Questa opzione verrà esplorata in una lezione futura per un modello addestrato da Scikit-learn - - **Utilizzo di Lobe.ai o Azure Custom vision**. Se si sta usando un sistema ML SaaS (Software as a Service) come [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) per addestrare un modello, questo tipo di software fornisce modi per esportare il modello per molte piattaforme, inclusa la creazione di un'API su misura da interrogare nel cloud dalla propria applicazione online. + - **Utilizzo di Lobe.ai o Azure Custom vision**. Se si sta usando un sistema ML SaaS (Software as a Service) come [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) per addestrare un modello, questo tipo di software fornisce modi per esportare il modello per molte piattaforme, inclusa la creazione di un'API su misura da interrogare nel cloud dalla propria applicazione online. Si ha anche l'opportunità di creare un'intera app Web Flask in grado di addestrare il modello stesso in un browser Web. Questo può essere fatto anche usando TensorFlow.js in un contesto JavaScript. @@ -37,7 +37,7 @@ Per questo scopo, poiché si è lavorato con i notebook basati su Python, verran Per questa attività sono necessari due strumenti: Flask e Pickle, entrambi eseguiti su Python. -✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di template per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) per esercitarsi a sviluppare con Flask. +✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di template per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) per esercitarsi a sviluppare con Flask. ✅ Cos'è [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 è un modulo Python che serializza e de-serializza la struttura di un oggetto Python. Quando si utilizza pickle in un modello, si serializza o si appiattisce la sua struttura per l'uso sul web. Cautela: pickle non è intrinsecamente sicuro, quindi si faccia attenzione se viene chiesto di de-serializzare un file. Un file creato con pickle ha il suffisso `.pkl`. 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 f5355dce..525e32ca 100644 --- a/3-Web-App/1-Web-App/translations/README.ja.md +++ b/3-Web-App/1-Web-App/translations/README.ja.md @@ -27,7 +27,7 @@ - **モデルの学習にはどのような技術が使われていますか？** 選択された技術は使用しなければいけないツールに影響を与える可能性があります。 - **Tensor flow を使っている。** 例えば TensorFlow を使ってモデルを学習している場合、 [TensorFlow.js](https://www.tensorflow.org/js/) を使って、Webアプリで使用できるように TensorFlow モデルを変換する機能をそのエコシステムは提供しています。 - **PyTorchを使っている。** [PyTorch](https://pytorch.org/) などのライブラリを使用してモデルを構築している場合、[ONNX](https://onnx.ai/) (Open Neural Network Exchange) 形式で出力して、JavaScript のWebアプリで [Onnx Runtime](https://www.onnxruntime.ai/) を使用するという選択肢があります。この選択肢は、Scikit-learn で学習したモデルを使う今後の講義で調べます。 - - **Lobe.ai または Azure Custom Vision を使っている。** [Lobe.ai](https://lobe.ai/) や [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) のような機械学習SaaS (Software as a Service) システムを使用してモデルを学習している場合、この種のソフトウェアは多くのプラットフォーム向けにモデルを出力する方法を用意していて、これにはクラウド上のオンラインアプリケーションからリクエストされるような専用APIを構築することも含まれます。 + - **Lobe.ai または Azure Custom Vision を使っている。** [Lobe.ai](https://lobe.ai/) や [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) のような機械学習SaaS (Software as a Service) システムを使用してモデルを学習している場合、この種のソフトウェアは多くのプラットフォーム向けにモデルを出力する方法を用意していて、これにはクラウド上のオンラインアプリケーションからリクエストされるような専用APIを構築することも含まれます。 また、ウェブブラウザ上でモデルを学習することができるFlaskのWebアプリを構築することもできます。JavaScript の場合でも TensorFlow.js を使うことで実現できます。 @@ -37,7 +37,7 @@ ここでの作業には2つのツールが必要です。FlaskとPickleで、どちらもPython上で動作します。 -✅ [Flask](https://palletsprojects.com/p/flask/) とは？制作者によって「マイクロフレームワーク」と定義されているFlaskは、Pythonを使ったWebフレームワークの基本機能と、Webページを構築するためのテンプレートエンジンを提供しています。Flaskでの構築を練習するために [この学習モジュール](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) を見てみてください。 +✅ [Flask](https://palletsprojects.com/p/flask/) とは？制作者によって「マイクロフレームワーク」と定義されているFlaskは、Pythonを使ったWebフレームワークの基本機能と、Webページを構築するためのテンプレートエンジンを提供しています。Flaskでの構築を練習するために [この学習モジュール](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) を見てみてください。 ✅ [Pickle](https://docs.python.org/3/library/pickle.html) とは？Pickle 🥒 は、Pythonのオブジェクト構造をシリアライズ・デシリアライズするPythonモジュールです。モデルを「塩漬け」にすると、Webで使用するためにその構造をシリアライズしたり平坦化したりします。pickleは本質的に安全ではないので、ファイルの 'un-pickle' を促された際は注意してください。塩漬けされたファイルの末尾は `.pkl` となります。 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 02f5fc54..4c7024ed 100644 --- a/3-Web-App/1-Web-App/translations/README.ko.md +++ b/3-Web-App/1-Web-App/translations/README.ko.md @@ -27,7 +27,7 @@ - **모델을 훈련시킬 때 사용하는 기술은 무엇인가요?** 선택된 기술은 사용할 도구에 영향을 줄 수 있습니다. - **Tensor flow 사용합니다.** 만약 TensorFlow로 모델을 훈련한다면, 예시로, 에코 시스템은 [TensorFlow.js](https://www.tensorflow.org/js/)로 웹 앱에서 사용할 TensorFlow 모델을 변환해주는 기능을 제공합니다. - **PyTorch 사용합니다.** 만약 [PyTorch](https://pytorch.org/) 같은 라이브러리로 모델을 만들면, [Onnx Runtime](https://www.onnxruntime.ai/)으로 할 수 있는 JavaScript 웹 앱에서 사용하기 위한 [ONNX](https://onnx.ai/) (Open Neural Network Exchange) 포맷으로 내보낼 옵션이 존재합니다. 이 옵션은 Scikit-learn-trained 모델로 이후 강의에서 알아볼 예정입니다. - - **Lobe.ai 또는 Azure Custom vision 사용합니다.** 만약 [Lobe.ai](https://lobe.ai/) 또는 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) 같은 ML SaaS (Software as a Service) 시스템으로 모델을 훈련하게 된다면, 이 소프트웨어 타입은 온라인 애플리케이션이 클라우드에서 쿼리된 bespoke API를 만드는 것도 포함해서 많은 플랫폼의 모델들을 내보낼 방식을 제공합니다. + - **Lobe.ai 또는 Azure Custom vision 사용합니다.** 만약 [Lobe.ai](https://lobe.ai/) 또는 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) 같은 ML SaaS (Software as a Service) 시스템으로 모델을 훈련하게 된다면, 이 소프트웨어 타입은 온라인 애플리케이션이 클라우드에서 쿼리된 bespoke API를 만드는 것도 포함해서 많은 플랫폼의 모델들을 내보낼 방식을 제공합니다. 또 웹 브라우저에서 모델로만 훈련할 수 있는 모든 Flask 웹 앱을 만들 수 있습니다. JavaScript 컨텍스트에서 TensorFlow.js로 마무리 지을 수 있습니다. @@ -37,7 +37,7 @@ 작업에서, 2가지 도구가 필요합니다: Flask 와 Pickle은, 둘 다 Python에서 작동합니다. -✅ [Flask](https://palletsprojects.com/p/flask/)는 무엇일까요? 작성자가 'micro-framework'로 정의한, Flask는 Python으로 웹 프레임워크의 기본적인 기능과 웹 페이지를 만드는 템플릿 엔진을 제공합니다. [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)을 보고 Flask로 만드는 것을 연습합니다. +✅ [Flask](https://palletsprojects.com/p/flask/)는 무엇일까요? 작성자가 'micro-framework'로 정의한, Flask는 Python으로 웹 프레임워크의 기본적인 기능과 웹 페이지를 만드는 템플릿 엔진을 제공합니다. [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott)을 보고 Flask로 만드는 것을 연습합니다. ✅ [Pickle](https://docs.python.org/3/library/pickle.html)은 무엇일까요? Pickle 🥒은 Python 객체 구조를 serializes와 de-serializes하는 Python 모듈입니다. 모델을 'pickle'하게 되면, 웹에서 쓰기 위해서 serialize 또는 flatten합니다. 주의합시다: pickle은 원래 안전하지 않아서, 파일을 'un-pickle'한다고 나오면 조심합니다. pickled 파일은 접미사 `.pkl`로 있습니다. 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 22bd111b..31912a32 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 @@ -27,7 +27,7 @@ Existem muitas perguntas que você precisa fazer: - **Qual tecnologia foi usada para treinar o modelo?** A tecnologia escolhida pode influenciar o ferramental que você precisa usar. - **Usando o fluxo do Tensor.** Se você estiver treinando um modelo usando o TensorFlow, por exemplo, esse ecossistema oferece a capacidade de converter um modelo do TensorFlow para uso em um aplicativo da web usando [TensorFlow.js](https://www.tensorflow.org/js/). - **Usando o PyTorch.** Se você estiver construindo um modelo usando uma biblioteca como [PyTorch](https://pytorch.org/), você tem a opção de exportá-lo em formato [ONNX](https://onnx.ai/) (Troca de rede neural aberta (Open Neural Network Exchange)) para uso em aplicativos web JavaScript que podem usar o [Onnx Runtime](https://www.onnxruntime.ai/). Esta opção será explorada em uma lição futura para um modelo treinado para aprender com Scikit. - - **Usando Lobe.ai ou Azure Custom Vision.** Se você estiver usando um sistema ML SaaS (Software as a Service), como [Lobe.ai](https://lobe.ai/) ou [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) para treinar um modelo, este tipo de software fornece maneiras de exportar o modelo para muitas plataformas, incluindo a construção de uma API sob medida para ser consultada na nuvem por seu aplicativo online. + - **Usando Lobe.ai ou Azure Custom Vision.** Se você estiver usando um sistema ML SaaS (Software as a Service), como [Lobe.ai](https://lobe.ai/) ou [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) para treinar um modelo, este tipo de software fornece maneiras de exportar o modelo para muitas plataformas, incluindo a construção de uma API sob medida para ser consultada na nuvem por seu aplicativo online. Você também tem a oportunidade de construir um aplicativo web Flask inteiro que seria capaz de treinar o próprio modelo em um navegador da web. Isso também pode ser feito usando TensorFlow.js em um contexto JavaScript. @@ -37,7 +37,7 @@ Para nossos propósitos, já que estamos trabalhando com notebooks baseados em P Para esta tarefa, você precisa de duas ferramentas: Flask e Pickle, ambos executados em Python. -✅ O que é [Flask](https://palletsprojects.com/p/flask/)? Definido como um 'micro-framework' por seus criadores, o Flask fornece os recursos básicos de estruturas web usando Python e um mecanismo de modelagem para construir páginas web. Dê uma olhada [neste módulo de aprendizagem](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para praticar a construção com Flask. +✅ O que é [Flask](https://palletsprojects.com/p/flask/)? Definido como um 'micro-framework' por seus criadores, o Flask fornece os recursos básicos de estruturas web usando Python e um mecanismo de modelagem para construir páginas web. Dê uma olhada [neste módulo de aprendizagem](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para praticar a construção com Flask. ✅ O que é [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 é um módulo Python que serializa e desserializa a estrutura de um objeto Python. Quando você 'pickle' um modelo, serializa ou aplaina sua estrutura para uso na web. Tenha cuidado: pickle não é intrinsecamente seguro, então tome cuidado se for solicitado para ser feito um 'un-pickle' em um arquivo. Um arquivo tem o sufixo `.pkl`. 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 070065d3..587817b2 100644 --- a/3-Web-App/1-Web-App/translations/README.pt.md +++ b/3-Web-App/1-Web-App/translations/README.pt.md @@ -39,7 +39,7 @@ Para nossos propósitos, já que estamos trabalhando com notebooks baseados em P Para esta tarefa, você precisa de duas ferramentas: Flask e Pickle, ambos em Python. -O que é [Frasco](https://palletsprojects.com/p/flask/)? Definido como um 'microframework' por seus criadores, o Flask fornece as características básicas de frameworks web usando Python e um motor de modelagem para construir páginas web. Dê uma olhada em [este módulo de aprendizado](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) para praticar a construção com o Flask. +O que é [Frasco](https://palletsprojects.com/p/flask/)? Definido como um 'microframework' por seus criadores, o Flask fornece as características básicas de frameworks web usando Python e um motor de modelagem para construir páginas web. Dê uma olhada em [este módulo de aprendizado](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott) para praticar a construção com o Flask. ✅ O que é [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 é um módulo Python que serializa e desserializa uma estrutura de objeto Python. Ao "pichar" um modelo, você serializa ou achata sua estrutura para uso na web. Tenha cuidado: o pickle não é intrinsecamente seguro, portanto, tenha cuidado se for solicitado a `cancelar o pickle` de um arquivo. Um arquivo em conserto tem o sufixo `.pkl`. 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 44cc797e..1ed88068 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 @@ -27,7 +27,7 @@ - **使用什么技术来训练模型？** 所选的技术可能会影响你需要使用的工具。 - **使用 TensorFlow**。例如，如果你正在使用 TensorFlow 训练模型，则该生态系统提供了使用 [TensorFlow.js](https://www.tensorflow.org/js/) 转换 TensorFlow 模型以便在Web应用程序中使用的能力。 - **使用 PyTorch**。如果你使用 [PyTorch](https://pytorch.org/) 等库构建模型，则可以选择将其导出到 [ONNX](https://onnx.ai/)（开放神经网络交换）格式，用于可以使用 [Onnx Runtime](https://www.onnxruntime.ai/)的JavaScript Web 应用程序。此选项将在 Scikit-learn-trained 模型的未来课程中进行探讨。 - - **使用 Lobe.ai 或 Azure 自定义视觉**。如果你使用 ML SaaS（软件即服务）系统，例如 [Lobe.ai](https://lobe.ai/) 或 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) 来训练模型，这种类型的软件提供了为许多平台导出模型的方法，包括构建一个定制A PI，供在线应用程序在云中查询。 + - **使用 Lobe.ai 或 Azure 自定义视觉**。如果你使用 ML SaaS（软件即服务）系统，例如 [Lobe.ai](https://lobe.ai/) 或 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) 来训练模型，这种类型的软件提供了为许多平台导出模型的方法，包括构建一个定制A PI，供在线应用程序在云中查询。 你还有机会构建一个完整的 Flask Web 应用程序，该应用程序能够在 Web浏览器中训练模型本身。这也可以在 JavaScript 上下文中使用 TensorFlow.js 来完成。 @@ -37,7 +37,7 @@ 对于此任务，你需要两个工具：Flask 和 Pickle，它们都在 Python 上运行。 -✅ 什么是 [Flask](https://palletsprojects.com/p/flask/)？ Flask 被其创建者定义为“微框架”，它提供了使用 Python 和模板引擎构建网页的 Web 框架的基本功能。看看[本学习单元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)练习使用 Flask 构建应用程序。 +✅ 什么是 [Flask](https://palletsprojects.com/p/flask/)？ Flask 被其创建者定义为“微框架”，它提供了使用 Python 和模板引擎构建网页的 Web 框架的基本功能。看看[本学习单元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott)练习使用 Flask 构建应用程序。 ✅ 什么是 [Pickle](https://docs.python.org/3/library/pickle.html)？ Pickle🥒是一个 Python 模块，用于序列化和反序列化 Python 对象结构。当你“pickle”一个模型时，你将其结构序列化或展平以在 Web 上使用。小心：pickle 本质上不是安全的，所以如果提示“un-pickle”文件，请小心。生产的文件具有后缀 `.pkl`。 diff --git a/3-Web-App/1-Web-App/translations/README.zh-tw.md b/3-Web-App/1-Web-App/translations/README.zh-tw.md index d28d7d9c..111bd90c 100644 --- a/3-Web-App/1-Web-App/translations/README.zh-tw.md +++ b/3-Web-App/1-Web-App/translations/README.zh-tw.md @@ -27,7 +27,7 @@ - **使用什麽技術來訓練模型？** 所選的技術可能會影響你需要使用的工具。 - **使用 TensorFlow**。例如，如果你正在使用 TensorFlow 訓練模型，則該生態系統提供了使用 [TensorFlow.js](https://www.tensorflow.org/js/) 轉換 TensorFlow 模型以便在Web應用程序中使用的能力。 - **使用 PyTorch**。如果你使用 [PyTorch](https://pytorch.org/) 等庫構建模型，則可以選擇將其導出到 [ONNX](https://onnx.ai/)（開放神經網絡交換）格式，用於可以使用 [Onnx Runtime](https://www.onnxruntime.ai/)的JavaScript Web 應用程序。此選項將在 Scikit-learn-trained 模型的未來課程中進行探討。 - - **使用 Lobe.ai 或 Azure 自定義視覺**。如果你使用 ML SaaS（軟件即服務）系統，例如 [Lobe.ai](https://lobe.ai/) 或 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) 來訓練模型，這種類型的軟件提供了為許多平臺導出模型的方法，包括構建一個定製A PI，供在線應用程序在雲中查詢。 + - **使用 Lobe.ai 或 Azure 自定義視覺**。如果你使用 ML SaaS（軟件即服務）系統，例如 [Lobe.ai](https://lobe.ai/) 或 [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77952-leestott) 來訓練模型，這種類型的軟件提供了為許多平臺導出模型的方法，包括構建一個定製A PI，供在線應用程序在雲中查詢。 你還有機會構建一個完整的 Flask Web 應用程序，該應用程序能夠在 Web瀏覽器中訓練模型本身。這也可以在 JavaScript 上下文中使用 TensorFlow.js 來完成。 @@ -37,7 +37,7 @@ 對於此任務，你需要兩個工具：Flask 和 Pickle，它們都在 Python 上運行。 -✅ 什麽是 [Flask](https://palletsprojects.com/p/flask/)？ Flask 被其創建者定義為「微框架」，它提供了使用 Python 和模板引擎構建網頁的 Web 框架的基本功能。看看[本學習單元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)練習使用 Flask 構建應用程序。 +✅ 什麽是 [Flask](https://palletsprojects.com/p/flask/)？ Flask 被其創建者定義為「微框架」，它提供了使用 Python 和模板引擎構建網頁的 Web 框架的基本功能。看看[本學習單元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott)練習使用 Flask 構建應用程序。 ✅ 什麽是 [Pickle](https://docs.python.org/3/library/pickle.html)？ Pickle🥒是一個 Python 模塊，用於序列化和反序列化 Python 對象結構。當你「pickle」一個模型時，你將其結構序列化或展平以在 Web 上使用。小心：pickle 本質上不是安全的，所以如果提示「un-pickle」文件，請小心。生產的文件具有後綴 `.pkl`。 diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md index 434c4893..24544e95 100644 --- a/4-Classification/2-Classifiers-1/README.md +++ b/4-Classification/2-Classifiers-1/README.md @@ -101,11 +101,11 @@ So, which classifier should you choose? Often, running through several and looki ![comparison of classifiers](images/comparison.png) > Plots generated on Scikit-learn's documentation -> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) +> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) ### A better approach -A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices: +A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Here, we discover that, for our multiclass problem, we have some choices: ![cheatsheet for multiclass problems](images/cheatsheet.png) > A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options 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 8376311e..a7982fb0 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 @@ -605,13 +605,13 @@ "\r\n", "So, which classifier should you choose? Often, running through several and looking for a good result is a way to test.\r\n", "\r\n", - "> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)\r\n", + "> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\r\n", "\r\n", "Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.`\r\n", "\r\n", "### **A better approach**\r\n", "\r\n", - "A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:\r\n", + "A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Here, we discover that, for our multiclass problem, we have some choices:\r\n", "\r\n", "

\r\n", " AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) +> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.` ### **A better approach** -A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices: +A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Here, we discover that, for our multiclass problem, we have some choices: ![A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options](../../images/cheatsheet.png){width="500"} diff --git a/4-Classification/2-Classifiers-1/translations/README.es.md b/4-Classification/2-Classifiers-1/translations/README.es.md index 6e8a799e..e253d970 100644 --- a/4-Classification/2-Classifiers-1/translations/README.es.md +++ b/4-Classification/2-Classifiers-1/translations/README.es.md @@ -102,11 +102,11 @@ Así que, ¿qué clasificador deberías elegir? A menudo, el ejecutar varios y b ![Comparación de clasificadores](../images/comparison.png) > Gráficos generados en la documentación de Scikit-learn -> AutoML resuelve este problema de forma pulcra al ejecutar estas comparaciones en la nube, permitiéndote elegir el mejor algoritmo para tus datos. Pruébalo [aquí](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) +> AutoML resuelve este problema de forma pulcra al ejecutar estas comparaciones en la nube, permitiéndote elegir el mejor algoritmo para tus datos. Pruébalo [aquí](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) ### Un mejor enfoque -Una mejor forma a estar adivinando, es seguir las ideas de esta [hoja de trucos de ML](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Aquí, descubrimos que, para nuestro problema multiclase, tenemos algunas opciones: +Una mejor forma a estar adivinando, es seguir las ideas de esta [hoja de trucos de ML](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Aquí, descubrimos que, para nuestro problema multiclase, tenemos algunas opciones: ![Hoja de trucos para problemas multiclase](../images/cheatsheet.png) > Una sección de la hoja de trucos de algoritmos de Microsoft, detallando opciones de clasificación multiclase. diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md index d02d767c..6db66fea 100644 --- a/4-Classification/2-Classifiers-1/translations/README.it.md +++ b/4-Classification/2-Classifiers-1/translations/README.it.md @@ -102,11 +102,11 @@ Quale classificatore si dovrebbe scegliere? Spesso, scorrerne diversi e cercare ![confronto di classificatori](../images/comparison.png) > Grafici generati sulla documentazione di Scikit-learn -> AutoML risolve questo problema in modo ordinato eseguendo questi confronti nel cloud, consentendo di scegliere l'algoritmo migliore per i propri dati. Si può provare [qui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) +> AutoML risolve questo problema in modo ordinato eseguendo questi confronti nel cloud, consentendo di scegliere l'algoritmo migliore per i propri dati. Si può provare [qui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) ### Un approccio migliore -Un modo migliore che indovinare a caso, tuttavia, è seguire le idee su questo [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) scaricabile. Qui si scopre che, per questo problema multiclasse, si dispone di alcune scelte: +Un modo migliore che indovinare a caso, tuttavia, è seguire le idee su questo [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) scaricabile. Qui si scopre che, per questo problema multiclasse, si dispone di alcune scelte: ![cheatsheet per problemi multiclasse](../images/cheatsheet.png) > Una sezione dell'Algorithm Cheat Sheet di Microsoft, che descrive in dettaglio le opzioni di classificazione multiclasse diff --git a/4-Classification/2-Classifiers-1/translations/README.ko.md b/4-Classification/2-Classifiers-1/translations/README.ko.md index bbebca7e..e0c247aa 100644 --- a/4-Classification/2-Classifiers-1/translations/README.ko.md +++ b/4-Classification/2-Classifiers-1/translations/README.ko.md @@ -103,11 +103,11 @@ Scikit-learn은 Supervised Learning 아래에 classification 그룹으로 묶여 ![comparison of classifiers](../images/comparison.png) > Plots generated on Scikit-learn's documentation -> AutoML은 클라우드에서 comparisons을 실행해서 이러한 문제를 깔끔하게 해결했으며, 데이터에 적당한 알고리즘을 고를 수 있습니다. [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)에서 시도해봅니다. +> AutoML은 클라우드에서 comparisons을 실행해서 이러한 문제를 깔끔하게 해결했으며, 데이터에 적당한 알고리즘을 고를 수 있습니다. [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)에서 시도해봅니다. ### 더 괜찮은 접근법 -그러나, 성급히 추측하기보다 더 괜찮은 방식으로, 내려받을 수 있는 [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)의 아이디어를 따르는 것입니다. 여기, multiclass 문제에 대하여, 몇 선택 사항을 볼 수 있습니다: +그러나, 성급히 추측하기보다 더 괜찮은 방식으로, 내려받을 수 있는 [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott)의 아이디어를 따르는 것입니다. 여기, multiclass 문제에 대하여, 몇 선택 사항을 볼 수 있습니다: ![cheatsheet for multiclass problems](../images/cheatsheet.png) > multiclass classification 옵션을 잘 설명하는, Microsoft의 알고리즘 치트 시트의 섹션 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 afbf15c7..21d7dc01 100644 --- a/4-Classification/2-Classifiers-1/translations/README.pt-br.md +++ b/4-Classification/2-Classifiers-1/translations/README.pt-br.md @@ -102,11 +102,11 @@ Então, qual classificador você deve escolher? Freqüentemente, percorrer vári ![comparação de classificadores](../images/comparison.png) > Plots gerados na documentação do Scikit-learn -> O AutoML resolve esse problema perfeitamente executando essas comparações na nuvem, permitindo que você escolha o melhor algoritmo para seus dados. Teste-o [aqui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa). +> O AutoML resolve esse problema perfeitamente executando essas comparações na nuvem, permitindo que você escolha o melhor algoritmo para seus dados. Teste-o [aqui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott). ### Uma abordagem melhor -Melhor do que adivinhar, é seguir as ideias nesta [planilha com dicas de ML](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Aqui, descobrimos que para o nosso problema multiclasse, temos algumas opções: +Melhor do que adivinhar, é seguir as ideias nesta [planilha com dicas de ML](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Aqui, descobrimos que para o nosso problema multiclasse, temos algumas opções: ![planilha com dicas para problemas multiclasse](../images/cheatsheet.png) > Uma planilha com dicas de algoritmo da Microsoft, detalhando as opções de classificação multiclasse. diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md index a4c42132..4a5ffde7 100644 --- a/4-Classification/2-Classifiers-1/translations/README.tr.md +++ b/4-Classification/2-Classifiers-1/translations/README.tr.md @@ -101,11 +101,11 @@ Scikit-learn, sınıflandırmayı gözetimli öğrenme altında grupluyor. Bu ka ![sınıflandırıcıların karşılaştırılması](../images/comparison.png) > Grafikler Scikit-learn dokümantasyonlarında oluşturulmuştur. -> AutoML, bu karşılaştırmaları bulutta çalıştırarak bu problemi muntazam bir şekilde çözer ve veriniz için en iyi algoritmayı seçmenizi sağlar. [Buradan](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) deneyin. +> AutoML, bu karşılaştırmaları bulutta çalıştırarak bu problemi muntazam bir şekilde çözer ve veriniz için en iyi algoritmayı seçmenizi sağlar. [Buradan](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) deneyin. ### Daha iyi bir yaklaşım -Böyle tahminlerle çözmekten daha iyi bir yol ise, indirilebilir [ML Kopya kağıdı](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) içindeki fikirlere bakmaktır. Burada, bizim çok sınıflı problemimiz için bazı seçenekler olduğunu görüyoruz: +Böyle tahminlerle çözmekten daha iyi bir yol ise, indirilebilir [ML Kopya kağıdı](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) içindeki fikirlere bakmaktır. Burada, bizim çok sınıflı problemimiz için bazı seçenekler olduğunu görüyoruz: ![çok sınıflı problemler için kopya kağıdı](../images/cheatsheet.png) > Microsoft'un Algoritma Kopya Kağıdı'ndan, çok sınıflı sınıflandırma seçeneklerini detaylandıran bir bölüm 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 7f220b92..4add5d4f 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -101,11 +101,11 @@ Scikit_learn 将分类任务归在了监督学习类别中，在这个类别中 ![各分类器比较](../images/comparison.png) > 图表来源于 Scikit-learn 的官方文档 -> AutoML 通过在云端运行这些算法并进行了对比，非常巧妙地解决的算法选择的问题，能帮助你根据数据集的特点来选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。 +> AutoML 通过在云端运行这些算法并进行了对比，非常巧妙地解决的算法选择的问题，能帮助你根据数据集的特点来选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)了解更多。 ### 另外一种效果更佳的分类器选择方法 -比起无脑地猜测，你可以下载这份[机器学习速查表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)。这里面将各算法进行了比较，能更有效地帮助我们选择算法。根据这份速查表，我们可以找到要完成本课程中涉及的多类型的分类任务，可以有以下这些选择： +比起无脑地猜测，你可以下载这份[机器学习速查表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott)。这里面将各算法进行了比较，能更有效地帮助我们选择算法。根据这份速查表，我们可以找到要完成本课程中涉及的多类型的分类任务，可以有以下这些选择： ![多类型问题速查表](../images/cheatsheet.png) > 微软算法小抄中部分关于多类型分类任务可选算法 diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md index 3e8a4137..48cc2329 100644 --- a/4-Classification/3-Classifiers-2/README.md +++ b/4-Classification/3-Classifiers-2/README.md @@ -228,7 +228,7 @@ Each of these techniques has a large number of parameters that you can tweak. Re ## Review & Self Study -There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology! +There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) of useful terminology! ## Assignment 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 260a35b2..f00acfb0 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 @@ -623,7 +623,7 @@ "\n", "### **Review & Self Study**\n", "\n", - "There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology!\n", + "There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) of useful terminology!\n", "\n", "#### THANK YOU TO:\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 112399a9..28a348af 100644 --- a/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd +++ b/4-Classification/3-Classifiers-2/solution/R/lesson_12.Rmd @@ -437,7 +437,7 @@ To find out more about a particular model and its parameters, use: `help("model" ### **Review & Self Study** -There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) of useful terminology! +There's a lot of jargon in these lessons, so take a minute to review [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) of useful terminology! #### THANK YOU TO: diff --git a/4-Classification/3-Classifiers-2/translations/README.es.md b/4-Classification/3-Classifiers-2/translations/README.es.md index ba3a942d..c49e1fd2 100644 --- a/4-Classification/3-Classifiers-2/translations/README.es.md +++ b/4-Classification/3-Classifiers-2/translations/README.es.md @@ -227,7 +227,7 @@ Cada una de estas técnicas tiene un gran número de parámetros que puedes modi ## Revisión y autoestudio -Existe mucha jerga en esta lecciones, ¡así que toma unos minutos para revisar [esta lista](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) de términos útiles! +Existe mucha jerga en esta lecciones, ¡así que toma unos minutos para revisar [esta lista](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) de términos útiles! ## Asignación diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md index 634ae550..5b49f6b1 100644 --- a/4-Classification/3-Classifiers-2/translations/README.it.md +++ b/4-Classification/3-Classifiers-2/translations/README.it.md @@ -228,7 +228,7 @@ Ognuna di queste tecniche ha un gran numero di parametri che si possono modifica ## Revisione e Auto Apprendimento -C'è molto gergo in queste lezioni, quindi si prenda un minuto per rivedere [questo elenco](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) di terminologia utile! +C'è molto gergo in queste lezioni, quindi si prenda un minuto per rivedere [questo elenco](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) di terminologia utile! ## Compito diff --git a/4-Classification/3-Classifiers-2/translations/README.ko.md b/4-Classification/3-Classifiers-2/translations/README.ko.md index 13968b5f..f4319a9c 100644 --- a/4-Classification/3-Classifiers-2/translations/README.ko.md +++ b/4-Classification/3-Classifiers-2/translations/README.ko.md @@ -228,7 +228,7 @@ weighted avg 0.73 0.72 0.72 1199 ## 검토 & 자기주도 학습 -강의에서 많은 특수 용어가 있어서, 잠시 시간을 투자해서 유용한 용어의 [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa)를 검토합니다! +강의에서 많은 특수 용어가 있어서, 잠시 시간을 투자해서 유용한 용어의 [this list](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott)를 검토합니다! ## 과제 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 7693c0e8..5d33a7fa 100644 --- a/4-Classification/3-Classifiers-2/translations/README.pt-br.md +++ b/4-Classification/3-Classifiers-2/translations/README.pt-br.md @@ -228,7 +228,7 @@ Cada uma dessas técnicas possui um grande número de parâmetros. Pesquise os p ## Revisão e Auto Aprendizagem -Há muitos termos nessas lições, então reserve um minuto para revisar [esta lista útil](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) sobre terminologias! +Há muitos termos nessas lições, então reserve um minuto para revisar [esta lista útil](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) sobre terminologias! ## Tarefa diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md index c0e3aa3d..270fa60e 100644 --- a/4-Classification/3-Classifiers-2/translations/README.tr.md +++ b/4-Classification/3-Classifiers-2/translations/README.tr.md @@ -228,7 +228,7 @@ Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. ## Gözden Geçirme & Kendi Kendine Çalışma -Bu derslerde çok fazla jargon var, bu yüzden yararlı terminoloji içeren [bu listeyi](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) incelemek için bir dakika ayırın. +Bu derslerde çok fazla jargon var, bu yüzden yararlı terminoloji içeren [bu listeyi](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) incelemek için bir dakika ayırın. ## Ödev 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 e75877b0..51cf64cf 100644 --- a/4-Classification/3-Classifiers-2/translations/README.zh-cn.md +++ b/4-Classification/3-Classifiers-2/translations/README.zh-cn.md @@ -228,7 +228,7 @@ weighted avg 0.73 0.72 0.72 1199 ## 回顾与自学 -课程中出现了很多术语，花点时间浏览[术语表](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa)来复习一下它们吧！ +课程中出现了很多术语，花点时间浏览[术语表](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott)来复习一下它们吧！ ## 作业 diff --git a/4-Classification/README.md b/4-Classification/README.md index 9d88feee..3ef7ea8c 100644 --- a/4-Classification/README.md +++ b/4-Classification/README.md @@ -11,7 +11,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le In this section, you will build on your earlier study of Regression and learn about other classifiers that you can use to better understand the data. -> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lessons diff --git a/4-Classification/translations/README.es.md b/4-Classification/translations/README.es.md index 8665efeb..d8fdcd46 100644 --- a/4-Classification/translations/README.es.md +++ b/4-Classification/translations/README.es.md @@ -11,7 +11,7 @@ En Asia y la India, las tradiciones alimentarias son muy diversas, ¡y muy delic Esta sección, se basará en el estudio anterior de la Regresión y aprenderás sobre otros clasificadores que puedes usar para entender mejor los datos. -Hay herramientas "low code" útiles que pueden ayudarte a aprender a trabajar con modelos de clasificación. Prueba [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +Hay herramientas "low code" útiles que pueden ayudarte a aprender a trabajar con modelos de clasificación. Prueba [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lecciones diff --git a/4-Classification/translations/README.hi.md b/4-Classification/translations/README.hi.md index e8916605..91c0357b 100644 --- a/4-Classification/translations/README.hi.md +++ b/4-Classification/translations/README.hi.md @@ -12,7 +12,7 @@ इस खंड में आप इस पाठ्यक्रम के पूर्व भाग में सीखे गए प्रतिगमन (regression) के कौशल पर निर्माण करेंगे, और अन्य वर्गीकारकों (classifiers) के बारे में जानेंगे। इन वर्गीकारकों का उपयोग करके आप डेटा (data) को बेहतर ढंग से समझ सकते हैं। -> कई उपकरण हैं जिनकी सहयता से आप न्यूनतम कोड के माध्यम से वर्गीकरण मॉडलों के साथ काम करना सीख सकते हैं। [इस कार्य के लिए अज़ौर म. ल. (Azure ML) का उपयोग करें।](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> कई उपकरण हैं जिनकी सहयता से आप न्यूनतम कोड के माध्यम से वर्गीकरण मॉडलों के साथ काम करना सीख सकते हैं। [इस कार्य के लिए अज़ौर म. ल. (Azure ML) का उपयोग करें।](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## पाठ diff --git a/4-Classification/translations/README.it.md b/4-Classification/translations/README.it.md index 1a8e6d4f..0edf03ec 100644 --- a/4-Classification/translations/README.it.md +++ b/4-Classification/translations/README.it.md @@ -11,7 +11,7 @@ In Asia e in India, le tradizioni alimentari sono estremamente diverse e molto d In questa sezione si approfondiranno le abilità sulla regressione apprese nella prima parte di questo programma di studi per conoscere altri classificatori da poter utilizzare e che aiuteranno a conoscere i propri dati. -> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lezioni diff --git a/4-Classification/translations/README.ko.md b/4-Classification/translations/README.ko.md index 9a77657f..4ecd0437 100644 --- a/4-Classification/translations/README.ko.md +++ b/4-Classification/translations/README.ko.md @@ -11,7 +11,7 @@ 이 섹션에서, 데이터를 배우며 도음이 될 다른 classifiers을 배우기 위해서 이 커리큘럼 첫 파트에서 배운 regression의 모든 내용을 바탕으로 진행합니다. -> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## 강의 diff --git a/4-Classification/translations/README.pt-br.md b/4-Classification/translations/README.pt-br.md index 0e0fa289..e63f6b69 100644 --- a/4-Classification/translations/README.pt-br.md +++ b/4-Classification/translations/README.pt-br.md @@ -11,7 +11,7 @@ Na Ásia e na Índia, as tradições alimentares são extremamente diversificada Você desenvolverá as habilidades que aprendeu nas lições sobre Regressão, para aprender sobre outros classificadores que o ajudarão a aprender mais sobre dados. -> Existem ferramentas _low-code_ (que não exigem o uso de código) úteis que podem ajudá-lo a aprender como trabalhar com modelos de classificação. Experimente a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Existem ferramentas _low-code_ (que não exigem o uso de código) úteis que podem ajudá-lo a aprender como trabalhar com modelos de classificação. Experimente a [Azure ML para esta tarefa](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ## Lições diff --git a/4-Classification/translations/README.ru.md b/4-Classification/translations/README.ru.md index f544c680..611291da 100644 --- a/4-Classification/translations/README.ru.md +++ b/4-Classification/translations/README.ru.md @@ -10,7 +10,7 @@ В этом разделе вы воспользуетесь навыками, полученными в первой части учебной программы о регрессии, и узнаете о других классификаторах, которые вы можете использовать и которые помогут вам понять ваши данные. -> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях классификации. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях классификации. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ## Уроки diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md index 9514dd0a..c7dcc311 100644 --- a/4-Classification/translations/README.tr.md +++ b/4-Classification/translations/README.tr.md @@ -10,7 +10,7 @@ Asya ve Hindistan'da yemek gelenekleri fazlaca çeşitlilik gösterir ve çok le Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk bölümünde öğrendiğiniz becerilere dayanıp onların üstüne beceriler ekleyeceksiniz ve veriniz hakkında bilgi sahibi olmanızı sağlayacak diğer sınıflandırıcıları öğreneceksiniz. -> Sınıflandırma modelleriyle çalışmayı öğrenmenizi sağlayacak faydalı düşük kodlu araçlar vardır. [Bu görev için Azure ML](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)'i deneyin. +> Sınıflandırma modelleriyle çalışmayı öğrenmenizi sağlayacak faydalı düşük kodlu araçlar vardır. [Bu görev için Azure ML](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)'i deneyin. ## Dersler diff --git a/4-Classification/translations/README.zh-cn.md b/4-Classification/translations/README.zh-cn.md index a24a411b..d69ca3fe 100644 --- a/4-Classification/translations/README.zh-cn.md +++ b/4-Classification/translations/README.zh-cn.md @@ -11,7 +11,7 @@ 建立在我们之前关于回归问题的讨论基础上，在本小节中，你将继续学习能够帮助你更好地理解数据的各种分类器。 -> 这里有一些不太涉及代码，且能帮助你了解如何使用分类模型的小工具。可以试试用 Azure 来完成[这个小任务](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)。 +> 这里有一些不太涉及代码，且能帮助你了解如何使用分类模型的小工具。可以试试用 Azure 来完成[这个小任务](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)。 ## 课程 diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index 1e17b15b..480aaef3 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -26,7 +26,7 @@ Alternately, you could use it for grouping search results - by shopping links, i ✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it? -Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) +Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott) ## Getting started with clustering [Scikit-learn offers a large array](https://scikit-learn.org/stable/modules/clustering.html) of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-learn and their appropriate use cases: 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 c38f6f96..1a544104 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb +++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb @@ -62,7 +62,7 @@ ">\r\n", "> Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.\r\n", "\r\n", - "Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)\r\n", + "Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\r\n", "\r\n", "### **Clustering algorithms**\r\n", "\r\n", diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd index 0eb57173..f2b5551f 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd +++ b/5-Clustering/1-Visualize/solution/R/lesson_14.Rmd @@ -64,7 +64,7 @@ Alternately, you could use it for grouping search results - by shopping links, i > > Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density. -Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) +Deepen your understanding of clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott) ### **Clustering algorithms** diff --git a/5-Clustering/1-Visualize/translations/README.es.md b/5-Clustering/1-Visualize/translations/README.es.md index d46894e6..3ee3dc6e 100644 --- a/5-Clustering/1-Visualize/translations/README.es.md +++ b/5-Clustering/1-Visualize/translations/README.es.md @@ -28,7 +28,7 @@ Alternativamente, puedes usarlo para agrupar resultados de búsqueda - por enlac ✅ Una vez que tus datos están organizados en grupos , asignale un Id de grupo, y esta técnica puede ser útil cuando conservas la privacidad de un conjunto de datos; en su lugar te puedes referir a un punto de datos por su id de grupo, en vez de sus datos identificables más reveladores. ¿Puedes pensar en otras razones del por qué preferirías un Id de grupo en lugar de otros elementos del grupo para identificarlo? -Profundiza tu compresión de las técnicas de agrupamiento en este [módulo de aprendizaje](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) +Profundiza tu compresión de las técnicas de agrupamiento en este [módulo de aprendizaje](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott) ## Empezando con el agrupamiento diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md index e3770789..007b2162 100644 --- a/5-Clustering/1-Visualize/translations/README.it.md +++ b/5-Clustering/1-Visualize/translations/README.it.md @@ -27,7 +27,7 @@ In alternativa, lo si può utilizzare per raggruppare i risultati di ricerca, ad ✅ Una volta che i dati sono organizzati in cluster, viene assegnato un ID cluster e questa tecnica può essere utile quando si preserva la privacy di un insieme di dati; si può invece fare riferimento a un punto dati tramite il suo ID cluster, piuttosto che dati identificabili più rivelatori. Si riesce a pensare ad altri motivi per cui fare riferimento a un ID cluster piuttosto che ad altri elementi del cluster per identificarlo? -In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) si approfondirà la propria comprensione delle tecniche di clustering +In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott) si approfondirà la propria comprensione delle tecniche di clustering ## Iniziare con il clustering diff --git a/5-Clustering/1-Visualize/translations/README.ko.md b/5-Clustering/1-Visualize/translations/README.ko.md index b6a36b20..6fae1dad 100644 --- a/5-Clustering/1-Visualize/translations/README.ko.md +++ b/5-Clustering/1-Visualize/translations/README.ko.md @@ -28,7 +28,7 @@ Clustering이 데이터셋에 라벨을 붙이지 않거나 입력이 미리 정 ✅ 데이터가 클러스터에서 구성되면, 클러스터 ID를 할당하며, 이 기술로 데이터셋의 프라이버시를 보호할 때 유용합니다; 식별할 수 있는 데이터를 더 노출하는 대신, 클러스터 ID로 데이터 포인트를 참조할 수 있습니다. 클러스터의 다른 요소가 아닌 클러스터 ID를 참조해서 식별하는 이유를 생각할 수 있나요? -이 [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)에서 clustering 기술을 깊게 이해합니다. +이 [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)에서 clustering 기술을 깊게 이해합니다. ## Clustering 시작하기 diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md index 98d22db4..5b699c62 100644 --- a/5-Clustering/1-Visualize/translations/README.zh-cn.md +++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md @@ -28,7 +28,7 @@ ✅一旦你的数据被组织成聚类，你就为它分配一个聚类 ID，这个技术在保护数据集的隐私时很有用；您可以改为通过其聚类 ID 来引用数据点，而不是通过更多的可明显区分的数据。您能想到为什么要引用聚类 ID 而不是聚类的其他元素来识别它的其他原因吗？ -在此[学习模块中](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)加深您对聚类技术的理解 +在此[学习模块中](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)加深您对聚类技术的理解 ## 聚类入门 diff --git a/5-Clustering/README.md b/5-Clustering/README.md index e93e7d7e..c511d1d0 100644 --- a/5-Clustering/README.md +++ b/5-Clustering/README.md @@ -12,7 +12,7 @@ Nigeria's diverse audience has diverse musical tastes. Using data scraped from S In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns. -> There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lessons diff --git a/5-Clustering/translations/README.es.md b/5-Clustering/translations/README.es.md index 1907c160..0a3c4cf7 100644 --- a/5-Clustering/translations/README.es.md +++ b/5-Clustering/translations/README.es.md @@ -13,7 +13,7 @@ La audiencia Nigeriana tiene diversos gustos musicales. Usando datos extraídos En esta serie de lecciones, descubrirás nuevas formas de analizar datos usando técnicas de clustering. El clustering es particularmente útil cuando tu conjunto de datos carece de etiquetas. Si este sí tiene etiquetas, entonces las técnicas de clasificación como las que has aprendido en lecciones previas son más útiles. Pero en casos donde pretendes agrupar datos sin etiquetas, el clustering es una gran forma de descubrir patrones. -> Existen herramientas low-code útiles que te pueden ayudar a trabajar con modelos de clustering. Prueba [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Existen herramientas low-code útiles que te pueden ayudar a trabajar con modelos de clustering. Prueba [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lecciones diff --git a/5-Clustering/translations/README.hi.md b/5-Clustering/translations/README.hi.md index 56c282e7..92f308e0 100644 --- a/5-Clustering/translations/README.hi.md +++ b/5-Clustering/translations/README.hi.md @@ -12,7 +12,7 @@ पाठों की इस श्रृंखला में, आप क्लस्टरिंग तकनीकों का उपयोग करके डेटा का विश्लेषण करने के नए तरीकों की खोज करेंगे। क्लस्टरिंग विशेष रूप से तब उपयोगी होती है जब आपके डेटासेट में लेबल की कमी होती है। यदि इसमें लेबल हैं, तो वर्गीकरण (Classification) तकनीकें जैसे कि आपने पिछले पाठों में सीखी हैं, अधिक उपयोगी हो सकती हैं। लेकिन ऐसे मामलों में जहां आप बिना लेबल वाले डेटा को समूहबद्ध करना चाहते हैं, क्लस्टरिंग पैटर्न खोजने का एक शानदार तरीका है। -> उपयोगी निम्न-कोड (low code) उपकरण हैं जो क्लस्टरिंग मॉडल के साथ काम करने के बारे में सीखने में आपकी सहायता कर सकते हैं। इसके लिए [अझूरे ऍम एल (Azure ML)](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) का प्रयोग करे +> उपयोगी निम्न-कोड (low code) उपकरण हैं जो क्लस्टरिंग मॉडल के साथ काम करने के बारे में सीखने में आपकी सहायता कर सकते हैं। इसके लिए [अझूरे ऍम एल (Azure ML)](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) का प्रयोग करे ## पाठ diff --git a/5-Clustering/translations/README.it.md b/5-Clustering/translations/README.it.md index 9aa64ceb..5074ce76 100644 --- a/5-Clustering/translations/README.it.md +++ b/5-Clustering/translations/README.it.md @@ -12,7 +12,7 @@ Il pubblico eterogeneo della Nigeria ha gusti musicali diversi. Usando i dati re In questa serie di lezioni si scopriranno nuovi modi per analizzare i dati utilizzando tecniche di clustering. Il clustering è particolarmente utile quando l'insieme di dati non ha etichette. Se ha etichette, le tecniche di classificazione come quelle apprese nelle lezioni precedenti potrebbero essere più utili. Ma nei casi in cui si sta cercando di raggruppare dati senza etichetta, il clustering è un ottimo modo per scoprire i modelli. -> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di clustering. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di clustering. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## Lezioni diff --git a/5-Clustering/translations/README.ko.md b/5-Clustering/translations/README.ko.md index e06a8277..1053ecc3 100644 --- a/5-Clustering/translations/README.ko.md +++ b/5-Clustering/translations/README.ko.md @@ -12,7 +12,7 @@ Clustering 은 서로 비슷한 오브젝트를 찾고 clusters 라고 불린 이 강의의 시리즈에서, clustering 기술로 데이터를 분석하는 새로운 방식을 찾아볼 예정입니다. Clustering 은 데이터셋에 라벨이 없으면 더욱 더 유용합니다. 만약 라벨이 있다면, 이전 강의에서 배운대로 classification 기술이 더 유용할 수 있습니다. 그러나 라벨링되지 않은 데이터를 그룹으로 묶으려면, clustering 은 패턴을 발견하기 위한 좋은 방식입니다. -> clustering 모델 작업을 배울 때 도움을 받을 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)를 시도해봅니다. +> clustering 모델 작업을 배울 때 도움을 받을 수 있는 유용한 low-code 도구가 있습니다. [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott)를 시도해봅니다. ## 강의 diff --git a/5-Clustering/translations/README.ru.md b/5-Clustering/translations/README.ru.md index 0a0da57a..7dcd5d9c 100644 --- a/5-Clustering/translations/README.ru.md +++ b/5-Clustering/translations/README.ru.md @@ -12,7 +12,7 @@ В этой серии уроков вы откроете для себя новые способы анализа данных с помощью методов кластеризации. Кластеризация особенно полезна, когда в наборе данных отсутствуют метки. Если в нем есть метки, тогда могут быть более полезными методы классификации, подобные тем, которые вы изучили на предыдущих уроках. Но в случаях, когда вы хотите сгруппировать данные без меток, кластеризация - отличный способ обнаружить закономерности. -> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях кластеризации. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa). +> Существуют инструменты, не требующие написания большого количества кода, которые могут помочь вам узнать о моделях кластеризации. Попробуйте [Azure ML для этой задачи](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott). ## Уроки 1. [Введение в кластеризацию](../1-Visualize/README.md) diff --git a/5-Clustering/translations/README.zh-cn.md b/5-Clustering/translations/README.zh-cn.md index cfe1d6f0..c82808dc 100644 --- a/5-Clustering/translations/README.zh-cn.md +++ b/5-Clustering/translations/README.zh-cn.md @@ -12,7 +12,7 @@ 在本系列课程中，您将发现使用聚类技术分析数据的新方法。当数据集缺少标签的时候，聚类特别有用。如果它有标签，那么分类技术(比如您在前面的课程中所学的那些)可能会更有用。但是如果要对未标记的数据进行分组，聚类是发现模式的好方法。 -> 这里有一些有用的低代码工具可以帮助您了解如何使用聚类模型。尝试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) +> 这里有一些有用的低代码工具可以帮助您了解如何使用聚类模型。尝试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-77952-leestott) ## 课程安排 diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md index ff16307d..81adfbbb 100644 --- a/6-NLP/1-Introduction-to-NLP/README.md +++ b/6-NLP/1-Introduction-to-NLP/README.md @@ -51,7 +51,7 @@ In this section, you will need, and use: python -m textblob.download_corpora ``` -> 💡 Tip: You can run Python directly in VS Code environments. Check the [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) for more information. +> 💡 Tip: You can run Python directly in VS Code environments. Check the [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott) for more information. ## Talking to machines 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 27f257da..5fa8d9ea 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.es.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.es.md @@ -51,7 +51,7 @@ En esta sección, necesitarás y usarás: python -m textblob.download_corpora ``` -> 💡 Consejo: Puedes ejecutar Python directamente en los ambientes de VS Code. Revisa la [documentación](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) para mayor información. +> 💡 Consejo: Puedes ejecutar Python directamente en los ambientes de VS Code. Revisa la [documentación](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott) para mayor información. ## Hablando con las máquinas 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 58cfc4cf..1104da7e 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.it.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md @@ -51,7 +51,7 @@ In questa sezione servirà e si utilizzerà: python -m textblob.download_corpora ``` -> 💡 Suggerimento: si può eseguire Python direttamente negli ambienti VS Code. Controllare la [documentazione](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) per ulteriori informazioni. +> 💡 Suggerimento: si può eseguire Python direttamente negli ambienti VS Code. Controllare la [documentazione](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott) per ulteriori informazioni. ## Parlare con le macchine 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 46e648fd..178117ff 100644 --- a/6-NLP/1-Introduction-to-NLP/translations/README.ko.md +++ b/6-NLP/1-Introduction-to-NLP/translations/README.ko.md @@ -51,7 +51,7 @@ python -m textblob.download_corpora ``` -> 💡 팁: VS Code 환경에서 Python을 바로 실행할 수 있습니다. [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)으로 정보를 더 확인합니다. +> 💡 팁: VS Code 환경에서 Python을 바로 실행할 수 있습니다. [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott)으로 정보를 더 확인합니다. ## 기계와 대화하기 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 20d26363..801e3787 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 @@ -55,7 +55,7 @@ Nesta seção, você vai precisar: python -m textblob.download_corpora ``` -> 💡 Dica: Você pode rodar Python diretamente nos ambientes (environments) do VS Code. Veja a [documentação](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) para mais informações. +> 💡 Dica: Você pode rodar Python diretamente nos ambientes (environments) do VS Code. Veja a [documentação](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott) para mais informações. ## Falando com máquinas 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 41bda36c..97428b49 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 @@ -49,7 +49,7 @@ python -m textblob.download_corpora ``` -> 💡 提示：你可以在 VS Code 环境中直接运行 Python。 点击 [文档](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) 查看更多信息。 +> 💡 提示：你可以在 VS Code 环境中直接运行 Python。 点击 [文档](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-77952-leestott) 查看更多信息。 ## 与机器对话 diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index 1ac39530..712ef731 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -180,7 +180,7 @@ Can you make Marvin even better by extracting other features from the user input ## Review & Self Study -There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Test some of the Pride and Prejudice sentences above and see if it can detect nuance. +There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-77952-leestott). Test some of the Pride and Prejudice sentences above and see if it can detect nuance. ## Assignment diff --git a/6-NLP/3-Translation-Sentiment/translations/README.es.md b/6-NLP/3-Translation-Sentiment/translations/README.es.md index a312a51c..58421434 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.es.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.es.md @@ -180,7 +180,7 @@ Aquí tienes una [solución de muestra](../solution/notebook.ipynb). ## Revisión y autoestudio -Hay varias formas de extraer el sentimiento del texto. Piensa en las aplicaciones de negocios que podrían hacer uso de esta técnica. Piensa cómo puede salir mal. Lee más acerca de los sistemas sofisticados listos para empresas que analizan el sentimiento tal como [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Prueba algunas de las oraciones de Orgullo y Prejuicio de arriba y observa si se pueden detectar matices. +Hay varias formas de extraer el sentimiento del texto. Piensa en las aplicaciones de negocios que podrían hacer uso de esta técnica. Piensa cómo puede salir mal. Lee más acerca de los sistemas sofisticados listos para empresas que analizan el sentimiento tal como [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-77952-leestott). Prueba algunas de las oraciones de Orgullo y Prejuicio de arriba y observa si se pueden detectar matices. ## Asignación diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md index 77a22410..a902fc2b 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.it.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md @@ -180,7 +180,7 @@ Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input ## Revisione e Auto Apprendimento -Esistono molti modi per estrarre il sentiment dal testo. Si pensi alle applicazioni aziendali che potrebbero utilizzare questa tecnica. Pensare a cosa potrebbe andare storto. Ulteriori informazioni sui sistemi sofisticati pronti per l'azienda che analizzano il sentiment come l'[analisi del testo di Azure](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Provare alcune delle frasi di Orgoglio e Pregiudizio sopra e vedere se può rilevare sfumature. +Esistono molti modi per estrarre il sentiment dal testo. Si pensi alle applicazioni aziendali che potrebbero utilizzare questa tecnica. Pensare a cosa potrebbe andare storto. Ulteriori informazioni sui sistemi sofisticati pronti per l'azienda che analizzano il sentiment come l'[analisi del testo di Azure](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-77952-leestott). Provare alcune delle frasi di Orgoglio e Pregiudizio sopra e vedere se può rilevare sfumature. ## Compito diff --git a/6-NLP/3-Translation-Sentiment/translations/README.ko.md b/6-NLP/3-Translation-Sentiment/translations/README.ko.md index e1828009..1ae01cdc 100644 --- a/6-NLP/3-Translation-Sentiment/translations/README.ko.md +++ b/6-NLP/3-Translation-Sentiment/translations/README.ko.md @@ -181,7 +181,7 @@ Darcy, as well as Elizabeth, really loved them; and they were ## 검토 & 자기주도 학습 -텍스트에서 감정을 추출하는 많은 방식이 있습니다. 이 기술로 사용할 수 있는 비지니스 애플리케이션을 생각해봅니다. 어떻게 틀릴 수 있는지도 생각해봅니다. [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa) 같이 감정 분석을 하는 정교한 enterprise-ready 시스템에 대하여 읽어봅니다. Pride and Prejudice 일부 문장에서 미묘한 차이를 감지할 수 있는지 테스트 합니다. +텍스트에서 감정을 추출하는 많은 방식이 있습니다. 이 기술로 사용할 수 있는 비지니스 애플리케이션을 생각해봅니다. 어떻게 틀릴 수 있는지도 생각해봅니다. [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-77952-leestott) 같이 감정 분석을 하는 정교한 enterprise-ready 시스템에 대하여 읽어봅니다. Pride and Prejudice 일부 문장에서 미묘한 차이를 감지할 수 있는지 테스트 합니다. ## 과제 diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index 151745aa..fd2ba705 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -397,7 +397,7 @@ This lesson demonstrates, as we saw in previous lessons, how critically importan ## Review & Self Study -Take [this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa) to discover tools to try when building speech and text-heavy models. +Take [this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77952-leestott) to discover tools to try when building speech and text-heavy models. ## Assignment 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 81b419b2..5cf3be9c 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.es.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.es.md @@ -408,7 +408,7 @@ Esta lección demuestra, como vimos en lecciones anteriores, qué tan críticame ## Revisión y autoestudio -Toma [esta ruta de aprendizaje de NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa) para descubrir herramientas a probar al construir modelos de voz y de gran cantidad de datos. +Toma [esta ruta de aprendizaje de NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77952-leestott) para descubrir herramientas a probar al construir modelos de voz y de gran cantidad de datos. ## Asignación 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 ff29d1b7..3e1bed6a 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md @@ -405,7 +405,7 @@ Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fond ## Revisione e Auto Apprendimento -Seguire [questo percorso di apprendimento su NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa) per scoprire gli strumenti da provare durante la creazione di modelli vocali e di testo. +Seguire [questo percorso di apprendimento su NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77952-leestott) per scoprire gli strumenti da provare durante la creazione di modelli vocali e di testo. ## Compito 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 78bb9fa6..681a46bd 100644 --- a/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md +++ b/6-NLP/4-Hotel-Reviews-1/translations/README.ko.md @@ -401,7 +401,7 @@ print("Loading took " + str(round(end - start, 2)) + " seconds") ## 검토 & 자기주도 학습 -[this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa)로 음성과 text-heavy 모델을 만들 때 시도하는 도구를 찾아봅니다. +[this Learning Path on NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77952-leestott)로 음성과 text-heavy 모델을 만들 때 시도하는 도구를 찾아봅니다. ## 과제 diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index c20d3340..38d32907 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -368,7 +368,7 @@ Now that you have your dataset analyzed for sentiment, see if you can use strate ## Review & Self Study -Take [this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa) to learn more and use different tools to explore sentiment in text. +Take [this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-77952-leestott) to learn more and use different tools to explore sentiment in text. ## Assignment [Try a different dataset](assignment.md) 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 00822c68..1791bfc5 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.es.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.es.md @@ -369,7 +369,7 @@ Ahora que tienes tu conjunto de datos analizado por sentimiento, observa si pued ## Revisión y autoestudio -Toma [este módulo de aprendizaje](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa) para aprender más y usar distintas herramientas para explorar el sentimiento en el texto. +Toma [este módulo de aprendizaje](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-77952-leestott) para aprender más y usar distintas herramientas para explorar el sentimiento en el texto. ## Asignación 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 c9a9fa00..e7d4d26f 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md @@ -369,7 +369,7 @@ Ora che si è analizzato il proprio insieme di dati per il sentiment, vedere se ## recensione e Auto Apprendimento -Seguire [questo modulo di apprendimento](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa) per saperne di più e utilizzare diversi strumenti per esplorare il sentiment nel testo. +Seguire [questo modulo di apprendimento](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-77952-leestott) per saperne di più e utilizzare diversi strumenti per esplorare il sentiment nel testo. ## Compito 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 22a1eb4f..2eec23aa 100644 --- a/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md +++ b/6-NLP/5-Hotel-Reviews-2/translations/README.ko.md @@ -369,7 +369,7 @@ df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False) ## 검토 & 자기주도 학습 -[this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa)로 더 배우고 다른 도구도 사용해서 텍스트에서 감정을 찾습니다. +[this Learn module](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-77952-leestott)로 더 배우고 다른 도구도 사용해서 텍스트에서 감정을 찾습니다. ## 과제 diff --git a/7-TimeSeries/1-Introduction/assignment.md b/7-TimeSeries/1-Introduction/assignment.md index 967ce8d5..a40dd613 100644 --- a/7-TimeSeries/1-Introduction/assignment.md +++ b/7-TimeSeries/1-Introduction/assignment.md @@ -2,7 +2,7 @@ ## Instructions -You've begun to learn about Time Series Forecasting by looking at the type of data that requires this special modeling. You've visualized some data around energy. Now, look around for some other data that would benefit from Time Series Forecasting. Find three examples (try [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)) and create a notebook to visualize them. Notate any special characteristics they have (seasonality, abrupt changes, or other trends) in the notebook. +You've begun to learn about Time Series Forecasting by looking at the type of data that requires this special modeling. You've visualized some data around energy. Now, look around for some other data that would benefit from Time Series Forecasting. Find three examples (try [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-77952-leestott)) and create a notebook to visualize them. Notate any special characteristics they have (seasonality, abrupt changes, or other trends) in the notebook. ## Rubric diff --git a/7-TimeSeries/1-Introduction/translations/assignment.es.md b/7-TimeSeries/1-Introduction/translations/assignment.es.md index 067fedaf..d9b8bfa0 100644 --- a/7-TimeSeries/1-Introduction/translations/assignment.es.md +++ b/7-TimeSeries/1-Introduction/translations/assignment.es.md @@ -2,7 +2,7 @@ ## Instrucciones -Has comenzado a aprender acerca de la predicción de series de tiempo al mirar el tipo de datos que requiere este modelado especial. Ya has visualizado algunos datos referentes a la energía. Ahora, busca otros datos que te beneficiarían de la predicción de series de tiempo. Encuentra tres ejemplos (prueba [Kaggle](https://kaggle.com) y [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)) y crea un notebook para visualizarlos. Anota cualquier característica especial que tengan (estacionalidad, cambios abruptos, u otras tendencias) en el notebook. +Has comenzado a aprender acerca de la predicción de series de tiempo al mirar el tipo de datos que requiere este modelado especial. Ya has visualizado algunos datos referentes a la energía. Ahora, busca otros datos que te beneficiarían de la predicción de series de tiempo. Encuentra tres ejemplos (prueba [Kaggle](https://kaggle.com) y [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-77952-leestott)) y crea un notebook para visualizarlos. Anota cualquier característica especial que tengan (estacionalidad, cambios abruptos, u otras tendencias) en el notebook. ## Rúbrica diff --git a/7-TimeSeries/1-Introduction/translations/assignment.it.md b/7-TimeSeries/1-Introduction/translations/assignment.it.md index cc192afd..28e9e816 100644 --- a/7-TimeSeries/1-Introduction/translations/assignment.it.md +++ b/7-TimeSeries/1-Introduction/translations/assignment.it.md @@ -2,7 +2,7 @@ ## Istruzioni -Si è iniziato a conoscere la previsione di serie temporali esaminando il tipo di dati richiesti da questa modellazione speciale. Si sono visualizzati alcuni dati sull'energia. Ora, cercare altri dati che potrebbero trarre vantaggio dalla previsione di serie temporali. Trovare tre esempi (provare [Kaggle](https://kaggle.com) e [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)) e creare un notebook per visualizzarli. Annotare nel notebook tutte le caratteristiche speciali che hanno (stagionalità, cambiamenti improvvisi o altre tendenze). +Si è iniziato a conoscere la previsione di serie temporali esaminando il tipo di dati richiesti da questa modellazione speciale. Si sono visualizzati alcuni dati sull'energia. Ora, cercare altri dati che potrebbero trarre vantaggio dalla previsione di serie temporali. Trovare tre esempi (provare [Kaggle](https://kaggle.com) e [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-77952-leestott)) e creare un notebook per visualizzarli. Annotare nel notebook tutte le caratteristiche speciali che hanno (stagionalità, cambiamenti improvvisi o altre tendenze). ## Rubrica diff --git a/7-TimeSeries/1-Introduction/translations/assignment.ko.md b/7-TimeSeries/1-Introduction/translations/assignment.ko.md index cc1cbf7d..d6d2bfa6 100644 --- a/7-TimeSeries/1-Introduction/translations/assignment.ko.md +++ b/7-TimeSeries/1-Introduction/translations/assignment.ko.md @@ -2,7 +2,7 @@ ## 설명 -이번 수업에서는 특수한 모델링이 필요한 유형의 (시계열) 데이터를 살펴봄으로써 시계열 예측에 대해 알아보기 시작했으며, 에너지 관련 데이터를 시각화해 보았습니다. 이 과제에서는 시계열 예측을 통해 유익한 결과를 도출해낼 수 있을 법한 다른 데이터를 찾아보세요. 세 가지 데이터를 찾아 ([Kaggle](https://kaggle.com)과 [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa)에서 한번 찾아보세요) 시각화하고 특징(계절성, 급변동성, 동향 등)을 관찰해 Jupyter Notebook(노트북)에 기록해 보시기 바랍니다. +이번 수업에서는 특수한 모델링이 필요한 유형의 (시계열) 데이터를 살펴봄으로써 시계열 예측에 대해 알아보기 시작했으며, 에너지 관련 데이터를 시각화해 보았습니다. 이 과제에서는 시계열 예측을 통해 유익한 결과를 도출해낼 수 있을 법한 다른 데이터를 찾아보세요. 세 가지 데이터를 찾아 ([Kaggle](https://kaggle.com)과 [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-77952-leestott)에서 한번 찾아보세요) 시각화하고 특징(계절성, 급변동성, 동향 등)을 관찰해 Jupyter Notebook(노트북)에 기록해 보시기 바랍니다. ## 평가기준표 diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index becfe1fe..3cf3e16d 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -98,7 +98,7 @@ https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full ### Motion sensing of animals -While deep learning has created a revolution in visually tracking animal movements (you can build your own [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa) here), classic ML still has a place in this task. +While deep learning has created a revolution in visually tracking animal movements (you can build your own [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-77952-leestott) here), classic ML still has a place in this task. Sensors to track movements of farm animals and IoT make use of this type of visual processing, but more basic ML techniques are useful to preprocess data. For example, in this paper, sheep postures were monitored and analyzed using various classifier algorithms. You might recognize the ROC curve on page 335. diff --git a/9-Real-World/1-Applications/translations/README.it.md b/9-Real-World/1-Applications/translations/README.it.md index 7b852038..a71f035f 100644 --- a/9-Real-World/1-Applications/translations/README.it.md +++ b/9-Real-World/1-Applications/translations/README.it.md @@ -97,7 +97,7 @@ https://www.frontiersin.org/articles/10.3389/fneur.2018.00006/pieno ### Rilevamento del movimento degli animali -Mentre il deep learning ha creato una rivoluzione nel tracciamento visivo dei movimenti degli animali (qui si può costruire il proprio [localizzatore di orsi polari](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa) ), il machine learning classico ha ancora un posto in questo compito. +Mentre il deep learning ha creato una rivoluzione nel tracciamento visivo dei movimenti degli animali (qui si può costruire il proprio [localizzatore di orsi polari](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-77952-leestott) ), il machine learning classico ha ancora un posto in questo compito. I sensori per tracciare i movimenti degli animali da fattoria e l'internet delle cose fanno uso di questo tipo di elaborazione visiva, ma tecniche di machine learning di base sono utili per preelaborare i dati. Ad esempio, in questo documento, le posture delle pecore sono state monitorate e analizzate utilizzando vari algoritmi di classificazione. Si potrebbe riconoscere la curva ROC a pagina 335. diff --git a/9-Real-World/1-Applications/translations/README.ko.md b/9-Real-World/1-Applications/translations/README.ko.md index fccce7a0..24d0fed0 100644 --- a/9-Real-World/1-Applications/translations/README.ko.md +++ b/9-Real-World/1-Applications/translations/README.ko.md @@ -98,7 +98,7 @@ https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full ### 동물의 움직임 감지 -딥러닝이 동물 움직임을 시각적으로-추적하려고 혁신적으로 만들었지만 (여기에 [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa)를 만들 수 있습니다), classic ML은 여전히 이 작업에서 자리를 차지하고 있습니다. +딥러닝이 동물 움직임을 시각적으로-추적하려고 혁신적으로 만들었지만 (여기에 [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-77952-leestott)를 만들 수 있습니다), classic ML은 여전히 이 작업에서 자리를 차지하고 있습니다. 농장 동물의 움직임을 추적하는 센서와 이 비주얼 프로세싱 타입을 사용하는 IoT도 있지만, 더 기본적인 ML 기술은 데이터를 전처리할 때 유용합니다. 예시로, 논문에서, 다양한 classifier 알고리즘으로 양의 상태를 모니터링하고 분석했습니다. 335 페이지에서 ROC curve를 알게 될 수 있습니다. diff --git a/README.md b/README.md index 0fce87fb..94612f2b 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ Travel with us around the world as we apply these classic techniques to data fro - Complete the assignment. - After completing a lesson group, visit the [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together. -> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths. +> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modules and learning paths. **Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum. diff --git a/SECURITY.md b/SECURITY.md index c5082ddf..c42d4e32 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -4,7 +4,7 @@ Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/previous-versions/tn-archive/cc751383(v=technet.10)?WT.mc_id=academic-15963-cxa), please report it to us as described below. +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/previous-versions/tn-archive/cc751383(v=technet.10)?WT.mc_id=academic-77952-leestott), please report it to us as described below. ## Reporting Security Issues diff --git a/translations/README.es.md b/translations/README.es.md index 03419a77..73f895ed 100644 --- a/translations/README.es.md +++ b/translations/README.es.md @@ -45,7 +45,7 @@ Nuestra metodología de enseñanza basada en proyectos, te permite aprender mien - Completa el ejercicio. - Después de completar un grupo de lecciones, visita el [tablero de discusión](https://github.com/microsoft/ML-For-Beginners/discussions) y "aprende en voz alta" llenando la rúbrica PAT apropiada. Un 'PAT' es una herramienta de evaluación del progreso que es una rúbrica la cual llenas para avanzar en tu aprendizaje. También puede reaccionar a otros PATs y así aprender juntos. -> Para aprender más, recomendamos seguir estos módulos y rutas de aprendizaje de [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa). +> Para aprender más, recomendamos seguir estos módulos y rutas de aprendizaje de [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). **Profesores**, hemos [incluido algunas sugerencias](../for-teachers.md) de cómo usar este plan de estudios. diff --git a/translations/README.hi.md b/translations/README.hi.md index de8835c6..8e87d72d 100644 --- a/translations/README.hi.md +++ b/translations/README.hi.md @@ -38,7 +38,7 @@ - असाइनमेंट पूरा करें। - एक पाठ समूह पूरा करने के बाद, [चर्चा बोर्ड](https://github.com/microsoft/ML-For-Beginners/discussions) पर जाएँ और उपयुक्त PAT रूब्रिक भरकर "ज़ोर से सीखें"। एक 'पीएटी' एक प्रगति आकलन उपकरण है जो एक रूब्रिक है जिसे आप अपने सीखने को आगे बढ़ाने के लिए भरते हैं। आप अन्य पीएटी पर भी प्रतिक्रिया कर सकते हैं ताकि हम एक साथ सीख सकें। -> आगे के अध्ययन के लिए, हम इन [माइक्रोसॉफ्ट लर्न](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) मॉड्यूल और सीखने के रास्तों का अनुसरण करने की सलाह देते हैं।. +> आगे के अध्ययन के लिए, हम इन [माइक्रोसॉफ्ट लर्न](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) मॉड्यूल और सीखने के रास्तों का अनुसरण करने की सलाह देते हैं।. **शिक्षक**, हमारे पास [कुछ सुझाव शामिल हैं](../for-teachers.md) इस पाठ्यक्रम का उपयोग कैसे करें। --- diff --git a/translations/README.it.md b/translations/README.it.md index 1bf67871..6825a4d7 100644 --- a/translations/README.it.md +++ b/translations/README.it.md @@ -38,7 +38,7 @@ Gira il mondo insieme a noi mentre applichiamo queste classiche tecniche ai dati - Completate il compito. - Dopo il completamento di un gruppo di lezioni, visitate il [Forum di discussione](https://github.com/microsoft/ML-For-Beginners/discussions) e "learn out load" (imparare ad alta voce) riempiendo la rubrica Pat appropriata. 'PAT' è uno strumento di valutazione dei progressi che consiste in una rubrica da compilare per promuovere il proprio apprendimento. Si può anche interagire in altri PAT in modo da imparare assieme. -> Per ulteriori approfondimenti, si raccomanda di sequire i seguenti moduli e percorsi di apprendimento [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa). +> Per ulteriori approfondimenti, si raccomanda di sequire i seguenti moduli e percorsi di apprendimento [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). **Insegnanti**, sono stati [inclusi alcuni suggerimenti](for-teachers.md) su come usare questo programma di studi. diff --git a/translations/README.ja.md b/translations/README.ja.md index 35faffdb..c7322da4 100644 --- a/translations/README.ja.md +++ b/translations/README.ja.md @@ -36,7 +36,7 @@ - 課題を完了させてください。 - レッスングループの完了後は [Discussionボード](https://github.com/microsoft/ML-For-Beginners/discussions) にアクセスし、適切なPAT表に記入することで「声に出して学習」してください。"PAT" とは Progress Assessment Tool（進捗評価ツール）の略で、学習を促進するために記入する表のことです。他のPATにリアクションすることもできるので、共に学ぶことが可能です。 -> さらに学習を進める場合は、[Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) のラーニングパスに従うことをお勧めします。 +> さらに学習を進める場合は、[Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) のラーニングパスに従うことをお勧めします。 **先生方**、このカリキュラムをどのように使用するか、[いくつかの提案](../for-teachers.md) があります。 diff --git a/translations/README.ko.md b/translations/README.ko.md index 5602333b..219b41fc 100644 --- a/translations/README.ko.md +++ b/translations/README.ko.md @@ -38,7 +38,7 @@ Microsoft의 Azure Cloud Advocates는 **Machine Learning**에 대한 모든 12- - 과제를 끝내봅니다. - 강의 그룹을 끝내면, [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions)를 방문하고 적절한 PAT rubric를 채워서 "learn out loud" 합니다. 'PAT'은 심화적으로 배우려고 작성하는 rubric인 Progress Assessment 도구 입니다. 같이 배울 수 있게 다른 PAT으로도 할 수 있습니다. -> 더 배우기 위해서, [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 모듈과 학습 경로를 따르는 것을 추천합니다. +> 더 배우기 위해서, [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 모듈과 학습 경로를 따르는 것을 추천합니다. **선생님**은, 이 커리큘럼의 사용 방법에 대해 [일부 제안사항](../for-teachers.md)이 있습니다. diff --git a/translations/README.ms.md b/translations/README.ms.md index 982245d5..10432769 100644 --- a/translations/README.ms.md +++ b/translations/README.ms.md @@ -38,7 +38,7 @@ Perjalanan bersama kami di seluruh dunia kerana kami menerapkan teknik klasik in - Selesaikan tugasan. - Setelah menyelesaikan kumpulan pelajaran, lawati [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar dengan kuat" dengan mengisi rubrik PAT yang sesuai. 'PAT' adalah Alat Penilaian Kemajuan yang merupakan rubrik yang anda isi untuk melanjutkan pembelajaran anda. Anda juga boleh bertindak balas terhadap PAT lain sehingga kami dapat belajar bersama. -> Untuk kajian lebih lanjut, kami mengesyorkan mengikuti [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) berikut dan jalan belajar. +> Untuk kajian lebih lanjut, kami mengesyorkan mengikuti [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) berikut dan jalan belajar. **Guru**, kami telah [memasukkan beberapa cadangan](for-teachers.md) mengenai cara menggunakan kurikulum ini. diff --git a/translations/README.pt-br.md b/translations/README.pt-br.md index 0c2b0831..1a55ea16 100644 --- a/translations/README.pt-br.md +++ b/translations/README.pt-br.md @@ -38,7 +38,7 @@ Viaje conosco ao redor do mundo enquanto aplicamos essas técnicas clássicas a - Conclua a tarefa. - Após concluir uma lição em grupo, visite o [Quadro de discussões](https://github.com/microsoft/ML-For-Beginners/discussions) e "aprenda em voz alta" preenchendo de forma apropriada a rubrica PAT. Um 'PAT' é uma ferramenta de avaliação de progresso que é uma rubrica que você preenche para promover seu aprendizado. Você também pode reagir a outros PATs para que possamos aprender juntos. -> Para um estudo mais aprofundado, recomendamos seguir os módulos e percursos de aprendizagem da [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa). +> Para um estudo mais aprofundado, recomendamos seguir os módulos e percursos de aprendizagem da [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). **Professores**, [incluímos algumas sugestões](../for-teachers.md) em como usar este curso. diff --git a/translations/README.pt.md b/translations/README.pt.md index 6a6ceb78..5616eb39 100644 --- a/translations/README.pt.md +++ b/translations/README.pt.md @@ -36,7 +36,7 @@ Viaja connosco ao redor do mundo enquanto aplicamos estas técnicas clássicas a - Conclui a tarefa - Depois de concluir uma aula em grupo, visita o [Quadro de discussão](https://github.com/microsoft/ML-For-Beginners/discussions) e "aprende em voz alta" preenchendo a rúbrica PAT apropriada. Um 'PAT' é uma ferramenta de avaliação de progresso que é uma rúbrica que preenches para promover a tua aprendizagem. Também podes reagir a outros PATs para que possamos aprender juntos. -> Para um estudo mais aprofundado, recomendamos que sigas estes [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) módulos e percursos de aprendizagem. +> Para um estudo mais aprofundado, recomendamos que sigas estes [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) módulos e percursos de aprendizagem. **Professores**, [incluímos algumas sugestões](../for-teachers.md) em como usar este curso. diff --git a/translations/README.tr.md b/translations/README.tr.md index c04ce5d9..44075aa2 100644 --- a/translations/README.tr.md +++ b/translations/README.tr.md @@ -35,7 +35,7 @@ Biz bu klasik teknikleri dünyanın birçok alanından verilere uygularken bizim - Ödevi tamamlayın - Bir ders grubunu tamamladıktan sonra, [Tartışma Panosu](https://github.com/microsoft/ML-For-Beginners/discussions)'nu ziyaret edin ve uygun PAT yönergesini doldurarak "sesli öğrenin" (Yani, tamamen öğrenmeden önce öğrenme süreciniz üzerine derin düşünerek içgözlem ve geridönütlerle kendinizde farkındalık oluşturun.). 'PAT', bir Progress Assessment Tool'dur (Süreç Değerlendirme Aracı), öğrenmenizi daha ileriye taşımak için doldurduğunuz bir yönergedir. Diğer PAT'lere de karşılık verebilirsiniz, böylece beraber öğrenebiliriz. -> İleri çalışma için, bu [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modüllerini ve öğrenme rotalarını takip etmenizi tavsiye ediyoruz. +> İleri çalışma için, bu [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) modüllerini ve öğrenme rotalarını takip etmenizi tavsiye ediyoruz. **Öğretmenler**, bu eğitim programının nasıl kullanılacağı hakkında [bazı öneriler ekledik](../for-teachers.md). diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md index b93625aa..f9186a05 100644 --- a/translations/README.zh-cn.md +++ b/translations/README.zh-cn.md @@ -35,7 +35,7 @@ - 完成作业 - 一节课完成后, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过填写相应的 PAT Rubric (课程目标) 来深化自己的学习成果。你也可以回应其它的 PAT，这样我们可以一起学习。 -> 如果希望进一步学习，我们推荐跟随 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模块和学习路径。 +> 如果希望进一步学习，我们推荐跟随 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 的模块和学习路径。 **对于老师们**，我们对于如何使用这套教程[提供了一些建议](../for-teachers.md)。 diff --git a/translations/Readme.ta.md b/translations/Readme.ta.md index 52acd885..f123e4ca 100644 --- a/translations/Readme.ta.md +++ b/translations/Readme.ta.md @@ -38,7 +38,7 @@ - வேலையை முடிக்கவும். - ஒரு பாடம் குழுவை முடித்த பிறகு, பார்வையிடவும் [கலந்துரையாடல் குழு](https://github.com/microsoft/ML-For-Beginners/discussions) மற்றும் "சத்தமாக கற்க" பொருத்தமான PAT rubric ஐ நிரப்புவதன் மூலம். ஒரு 'PAT' என்பது முன்னேற்ற மதிப்பீட்டுக் கருவியாகும், இது உங்கள் கற்றலை மேலும் மேம்படுத்த நீங்கள் நிரப்பும் ரூபிரிக் ஆகும். நீங்கள் மற்ற PAT களுக்கு எதிர்வினையாற்றலாம், எனவே நாங்கள் ஒன்றாகக் கற்றுக்கொள்ளலாம். -> மேலும் ஆய்வுக்கு, இவற்றைப் பின்பற்ற பரிந்துரைக்கிறோம் [மைக்ரோசாப்ட் கற்றல்](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa)தொகுதிகள் மற்றும் கற்றல் பாதைகள். +> மேலும் ஆய்வுக்கு, இவற்றைப் பின்பற்ற பரிந்துரைக்கிறோம் [மைக்ரோசாப்ட் கற்றல்](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott)தொகுதிகள் மற்றும் கற்றல் பாதைகள். **ஆசிரியர்கள்**, எங்களிடம் உள்ளது [சில பரிந்துரைகளை உள்ளடக்கியது](https://github.com/microsoft/ML-For-Beginners/blob/main/for-teachers.md) இந்த பாடத்திட்டத்தை எவ்வாறு பயன்படுத்துவது.