@ -107,7 +107,7 @@ The first step is to collect the data. While in many cases it can be a straight
Storing data can be challenging, especially if we are talking about big data. When deciding how to store data, it makes sense to anticipate the way you would to query the data in the future. There are several ways data can be stored:
<ul>
<li>A relational database stores a collection of tables, and uses a special language called SQL to query them. Typically, tables are organized into different groups called schemas. In many cases we need to convert the data from original form to fit the schema.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, such as <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, does not enforce schemas on data, and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, such as <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, does not enforce schemas on data, and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> storage is used for large collections of data in raw, unstructured form. Data lakes are often used with big data, where all data cannot fit on one machine, and has to be stored and processed by a cluster of servers. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> is the data format that is often used in conjunction with big data.</li>
@ -113,7 +113,7 @@ El primer paso es recoger los datos. Aunque en muchos casos puede ser un proces
El almacenamiento de datos puede ser un reto, especialmente si hablamos de big data. A la hora de decidir cómo almacenar los datos, tiene sentido anticiparse a la forma en que se consultarán los datos en el futuro. Hay varias formas de almacenar los datos:
<ul>
<li>Una base de datos relacional almacena una colección de tablas y utiliza un lenguaje especial llamado SQL para consultarlas. Normalmente, las tablas se organizan en diferentes grupos llamados esquemas. En muchos casos hay que convertir los datos de la forma original para que se ajusten al esquema.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">una base de datos no SQL</a>, como <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, no impone esquemas a los datos y permite almacenar datos más complejos, por ejemplo, documentos JSON jerárquicos o gráficos. Sin embargo, las bases de datos NoSQL no tienen las ricas capacidades de consulta de SQL, y no pueden asegurar la integridad referencial, i.e. reglas sobre cómo se estructuran los datos en las tablas y que rigen las relaciones entre ellas.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">una base de datos no SQL</a>, como <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, no impone esquemas a los datos y permite almacenar datos más complejos, por ejemplo, documentos JSON jerárquicos o gráficos. Sin embargo, las bases de datos NoSQL no tienen las ricas capacidades de consulta de SQL, y no pueden asegurar la integridad referencial, i.e. reglas sobre cómo se estructuran los datos en las tablas y que rigen las relaciones entre ellas.</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">Los lagos de datos</a> se utilizan para grandes colecciones de datos en bruto y sin estructurar. Los lagos de datos se utilizan a menudo con big data, donde los datos no caben en una sola máquina, y tienen que ser almacenados y procesados por un clúster de servidores. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> es el formato de datos que se suele utilizar junto con big data.</li>
डेटा स्टोर करना चुनौतीपूर्ण हो सकता है, खासकर अगर हम बड़े डेटा के बारे में बात कर रहे हैं। डेटा को स्टोर करने का तरीका तय करते समय, भविष्य में डेटा को क्वेरी करने के तरीके का अनुमान लगाना समझ में आता है। डेटा को स्टोर करने के कई तरीके हैं:
<ul>
<li>एक रिलेशनल डेटाबेस तालिकाओं के संग्रह को संग्रहीत करता है, और उन्हें क्वेरी करने के लिए SQL नामक एक विशेष भाषा का उपयोग करता है। आमतौर पर, तालिकाओं को विभिन्न समूहों में व्यवस्थित किया जाता है जिन्हें स्कीमा कहा जाता है। कई मामलों में हमें स्कीमा को फिट करने के लिए डेटा को मूल रूप से परिवर्तित करने की आवश्यकता होती है।</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">एक NoSQL</a> डेटाबेस, जैसे कि <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, करता है डेटा पर स्कीमा लागू नहीं करता है, और अधिक जटिल डेटा संग्रहीत करने की अनुमति देता है, उदाहरण के लिए, पदानुक्रमित JSON दस्तावेज़ या ग्राफ़। हालाँकि, NoSQL डेटाबेस में SQL की समृद्ध क्वेरी क्षमता नहीं होती है, और यह संदर्भात्मक अखंडता को लागू नहीं कर सकता है, अर्थात डेटा को तालिकाओं में कैसे संरचित किया जाता है और तालिकाओं के बीच संबंधों को नियंत्रित करने के नियम।</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">एक NoSQL</a> डेटाबेस, जैसे कि <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, करता है डेटा पर स्कीमा लागू नहीं करता है, और अधिक जटिल डेटा संग्रहीत करने की अनुमति देता है, उदाहरण के लिए, पदानुक्रमित JSON दस्तावेज़ या ग्राफ़। हालाँकि, NoSQL डेटाबेस में SQL की समृद्ध क्वेरी क्षमता नहीं होती है, और यह संदर्भात्मक अखंडता को लागू नहीं कर सकता है, अर्थात डेटा को तालिकाओं में कैसे संरचित किया जाता है और तालिकाओं के बीच संबंधों को नियंत्रित करने के नियम।</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">डेटा लेक</a> संग्रहण का उपयोग कच्चे, असंरचित रूप में डेटा के बड़े संग्रह के लिए किया जाता है। डेटा झीलों का उपयोग अक्सर बड़े डेटा के साथ किया जाता है, जहां सभी डेटा एक मशीन पर फिट नहीं हो सकते हैं, और सर्वरों के एक समूह द्वारा संग्रहीत और संसाधित किया जाना है। <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> डेटा प्रारूप है जिसे अक्सर बड़े डेटा के संयोजन में उपयोग किया जाता है।</li>
특히 빅 데이터의 경우에, 데이터를 저장하는 것은 어려울 수 있습니다. 데이터를 저장하는 방법을 결정할 때는 나중에 데이터를 쿼리할 방법을 예상하는 것이 좋습니다. 데이터를 저장할 수 있는 방법에는 여러 가지가 있습니다.
<ul>
<li>관계형 데이터베이스는 테이블 모음을 저장하고 SQL이라는 특수 언어를 사용하여 쿼리합니다. 일반적으로 테이블은 어떤 스키마를 사용하여 서로 연결됩니다. 많은 경우 스키마에 맞게 원래 형식의 데이터를 변환해야 합니다.</li>
<li><ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>와 같은 <ahref="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a> 데이터베이스는 데이터에 스키마를 적용하지 않으며, 계층적 JSON 문서 또는 그래프와 같은 더 복잡한 데이터를 저장할 수 있습니다. 그러나 NoSQL 데이터베이스는 SQL의 풍부한 쿼리 기능이 없으며 데이터 간의 참조 무결성을 강제할 수 없습니다.</li>
<li><ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>와 같은 <ahref="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a> 데이터베이스는 데이터에 스키마를 적용하지 않으며, 계층적 JSON 문서 또는 그래프와 같은 더 복잡한 데이터를 저장할 수 있습니다. 그러나 NoSQL 데이터베이스는 SQL의 풍부한 쿼리 기능이 없으며 데이터 간의 참조 무결성을 강제할 수 없습니다.</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> 저장소는 원시 형식(raw form)의 대규모 데이터 저장소로 사용됩니다. 데이터 레이크는 모든 데이터가 하나의 시스템에 들어갈 수 없고 클러스터에서 저장 및 처리를 해야하는 빅 데이터와 함께 사용하는 경우가 많습니다. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a>은 빅 데이터와 함께 자주 사용되는 데이터 형식입니다.</li>
@ -108,7 +108,7 @@ De eerste stap is het verzamelen van de gegevens. Hoewel het in veel gevallen e
Het opslaan van gegevens kan een uitdaging zijn, vooral als we het hebben over big data. Wanneer u beslist hoe u gegevens wilt opslaan, is het logisch om te anticiperen op de manier waarop u de gegevens in de toekomst zou opvragen. Er zijn verschillende manieren waarop gegevens kunnen worden opgeslagen:
<ul>
<li>Een relationele database slaat een verzameling tabellen op en gebruikt een speciale taal genaamd SQL om deze op te vragen. Tabellen zijn meestal georganiseerd in verschillene groepen die schema's worden genoemd. In veel gevallen moeten we de gegevens van de oorspronkelijke vorm converteren naar het schema.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, zoals <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, dwingt geen schema's af op gegevens en maakt het opslaan van complexere gegevens mogelijk, bijvoorbeeld hiërarchische JSON-documenten of grafieken. NoSQL-databases hebben echter niet de uitgebreide querymogelijkheden van SQL en kunnen geen referentiële integriteit afdwingen, d.w.z. regels over hoe de gegevens in tabellen zijn gestructureerd en de relaties tussen tabellen regelen.</li>
<li><ahref="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, zoals <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, dwingt geen schema's af op gegevens en maakt het opslaan van complexere gegevens mogelijk, bijvoorbeeld hiërarchische JSON-documenten of grafieken. NoSQL-databases hebben echter niet de uitgebreide querymogelijkheden van SQL en kunnen geen referentiële integriteit afdwingen, d.w.z. regels over hoe de gegevens in tabellen zijn gestructureerd en de relaties tussen tabellen regelen.</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> opslag wordt gebruikt voor grote verzamelingen gegevens in ruwe, ongestructureerde vorm. Data lakes worden vaak gebruikt met big data, waarbij alle data niet op één machine past en moet worden opgeslagen en verwerkt door een cluster van servers. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> is het gegevensformaat dat vaak wordt gebruikt in combinatie met big data.</li>
@ -107,7 +107,7 @@ Primeiro passo é coletar os dados. Enquanto em muitos casos isso pode ser um pr
Armazenar os dados pode ser desafiador, especialmente se estamos falando de big data. Enquanto decide como armazenar os dados, faz sentido antecipar a forma como você gostaria de consultá-los mais tarde. Existem diversas formas de como os dados podem ser armazenados:
<ul>
<li> Bancos de dados relacionais armazenam uma coleção de tabelas, e utilizam uma linguagem especial chamada SQL para consultá-los. Tipicamente, tabelas seriam conectadas umas às outras usando algum schema. Em vários casas nós precisamos converter os dados da forma original para ajustar al schema.</li>
<li>Bancos de dados <ahref="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a>, como <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>, não impõe schema nos dados, e permite o armazenamento de dados mais complexos, como por exemplo, documentos hierárquicos JSON ou grafos. No entanto, bancos de dados NoSQL não possuem a capacidade rica de consulta do SQL, e não podem impor integridade referencial entre os dados.</li>
<li>Bancos de dados <ahref="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a>, como <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, não impõe schema nos dados, e permite o armazenamento de dados mais complexos, como por exemplo, documentos hierárquicos JSON ou grafos. No entanto, bancos de dados NoSQL não possuem a capacidade rica de consulta do SQL, e não podem impor integridade referencial entre os dados.</li>
<li>Armazenamento em <ahref="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> é usado para grandes coleções de dados na forma bruta. Data lakes são frequentemente usados para big data, onde todos não podem se encaixar em uma máquina, e precisam ser armazenados e processados por um cluster. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> é o formato de dado que é frequentemente usado em conjunção com big data.</li>
<li>Реляционные базы данных хранят коллекцию таблиц и используют специальный язык запросов SQL. Обычно, таблицы соединены друг с другом по определённой схеме. Очень часто нам необходимо преобразовать данные, чтобы они подходили под схему.
</li>
<li><ahref="https://ru.wikipedia.org/wiki/NoSQL">Нереляционные (NoSQL)</a> базы данных, такие как <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>, не навязывают строгую модель данных и позволяют хранить более сложные данные, например иерархические JSON документы или графы. С другой стороны, нереляционные базы данных не имеют широких возможностей языка SQL и не гарантируют ссылочной целостности данных.
<li><ahref="https://ru.wikipedia.org/wiki/NoSQL">Нереляционные (NoSQL)</a> базы данных, такие как <ahref="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, не навязывают строгую модель данных и позволяют хранить более сложные данные, например иерархические JSON документы или графы. С другой стороны, нереляционные базы данных не имеют широких возможностей языка SQL и не гарантируют ссылочной целостности данных.
</li>
<li><ahref="https://en.wikipedia.org/wiki/Data_lake">Озеро данных</a> - хранилище, используемое для больших коллекций "сырых" данных. Озёра данных часто встречаются в больших данных, когда все данные не помещаются в память одного компьютера и их необходимо хранить и обрабатывать вычислительным кластером. <ahref="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> - формат данных, часто применяемый в связке с большими данными.
@ -170,11 +170,11 @@ There are numerous relational databases available on the internet. You can explo
## Review & Self Study
There are several resources available on [Microsoft Learn](https://docs.microsoft.com/learn?WT.mc_id=academic-40229-cxa) for you to continue your exploration of SQL and relational database concepts
There are several resources available on [Microsoft Learn](https://docs.microsoft.com/learn?WT.mc_id=academic-77958-bethanycheum) for you to continue your exploration of SQL and relational database concepts
- [Describe concepts of relational data](https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-40229-cxa)
- [Get Started Querying with Transact-SQL](https://docs.microsoft.com//learn/paths/get-started-querying-with-transact-sql?WT.mc_id=academic-40229-cxa) (Transact-SQL is a version of SQL)
- [SQL content on Microsoft Learn](https://docs.microsoft.com/learn/browse/?products=azure-sql-database%2Csql-server&expanded=azure&WT.mc_id=academic-40229-cxa)
- [Describe concepts of relational data](https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-77958-bethanycheum)
- [Get Started Querying with Transact-SQL](https://docs.microsoft.com//learn/paths/get-started-querying-with-transact-sql?WT.mc_id=academic-77958-bethanycheum) (Transact-SQL is a version of SQL)
- [SQL content on Microsoft Learn](https://docs.microsoft.com/learn/browse/?products=azure-sql-database%2Csql-server&expanded=azure&WT.mc_id=academic-77958-bethanycheum)
You have been provided a [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) built on [SQLite](https://sqlite.org/index.html) which contains information about airports. The schema is displayed below. You will use the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa) in [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-40229-cxa) to display information about different cities' airports.
You have been provided a [database](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) built on [SQLite](https://sqlite.org/index.html) which contains information about airports. The schema is displayed below. You will use the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) in [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) to display information about different cities' airports.
## Instructions
@ -10,8 +10,8 @@ To get started with the assignment, you'll need to perform a couple of steps. Yo
You can use Visual Studio Code and the SQLite extension to interact with the database.
1. Navigate to [code.visualstudio.com](https://code.visualstudio.com?WT.mc_id=academic-40229-cxa) and follow the instructions to install Visual Studio Code
1. Install the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa) extension as instructed on the Marketplace page
1. Navigate to [code.visualstudio.com](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) and follow the instructions to install Visual Studio Code
1. Install the [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) extension as instructed on the Marketplace page
### Download and open the database
@ -25,7 +25,7 @@ Next you will download an open the database.
Once open, the new query window can be used to run SQL statements against the database. You can use the command **Ctl-Shift-Q** (or **Cmd-Shift-Q** on a Mac) to run queries against the database.
> [!NOTE] For more information about the SQLite extension, you can consult the [documentation](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa)
> [!NOTE] For more information about the SQLite extension, you can consult the [documentation](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum)
आपके लिए SQL और रिलेशनल डेटाबेस अवधारणाओं की खोज जारी रखने के लिए [Microsoft Learn](https://docs.microsoft.com/learn?WT.mc_id=academic-40229-cxa) पर कई संसाधन उपलब्ध हैं
आपके लिए SQL और रिलेशनल डेटाबेस अवधारणाओं की खोज जारी रखने के लिए [Microsoft Learn](https://docs.microsoft.com/learn?WT.mc_id=academic-77958-bethanycheum) पर कई संसाधन उपलब्ध हैं
- [संबंधपरक डेटा की अवधारणाओं का वर्णन करें](https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-40229-cxa)
- [Transact-SQL के साथ क्वेरी करना प्रारंभ करें](https://docs.microsoft.com//learn/paths/get-started-querying-with-transact-sql?WT.mc_id=academic-40229-cxa) (ट्रांजैक्ट-एसक्यूएल एसक्यूएल का एक संस्करण है)
- [Microsoft पर SQL सामग्री जानें](https://docs.microsoft.com/learn/browse/?products=azure-sql-database%2Csql-server&expanded=azure&WT.mc_id=academic-40229-cxa)
- [संबंधपरक डेटा की अवधारणाओं का वर्णन करें](https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-77958-bethanycheum)
- [Transact-SQL के साथ क्वेरी करना प्रारंभ करें](https://docs.microsoft.com//learn/paths/get-started-querying-with-transact-sql?WT.mc_id=academic-77958-bethanycheum) (ट्रांजैक्ट-एसक्यूएल एसक्यूएल का एक संस्करण है)
- [Microsoft पर SQL सामग्री जानें](https://docs.microsoft.com/learn/browse/?products=azure-sql-database%2Csql-server&expanded=azure&WT.mc_id=academic-77958-bethanycheum)
आपको एक [डेटाबेस](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) प्रदान किया जायेगा। बनाया गया है [SQLite](https://sqlite.org/index.html) पर जिसमें हवाई अड्डों के बारे में जानकारी होती है। स्कीमा नीचे प्रदर्शित किया गया है। आप [विजुअल स्टूडियो कोड](https://code.visualstudio.com/) में [SQLite एक्सटेंशन](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa) का इस्तेमाल करेंगे। Visualstudio.com?WT.mc_id=academic-40229-cxa) विभिन्न शहरों के हवाई अड्डों के बारे में जानकारी प्रदर्शित करने के लिए।
आपको एक [डेटाबेस](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) प्रदान किया जायेगा। बनाया गया है [SQLite](https://sqlite.org/index.html) पर जिसमें हवाई अड्डों के बारे में जानकारी होती है। स्कीमा नीचे प्रदर्शित किया गया है। आप [विजुअल स्टूडियो कोड](https://code.visualstudio.com/) में [SQLite एक्सटेंशन](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) का इस्तेमाल करेंगे। Visualstudio.com?WT.mc_id=academic-77958-bethanycheum) विभिन्न शहरों के हवाई अड्डों के बारे में जानकारी प्रदर्शित करने के लिए।
## निर्देश
@ -10,8 +10,8 @@
आप डेटाबेस के साथ इंटरैक्ट करने के लिए विजुअल स्टूडियो कोड और SQLite एक्सटेंशन का उपयोग कर सकते हैं।
1. [code.visualstudio.com](https://code.visualstudio.com?WT.mc_id=academic-40229-cxa) पर नेविगेट करें और विजुअल स्टूडियो कोड इंस्टॉल करने के लिए निर्देशों का पालन करें
1. मार्केटप्लेस पेज पर दिए निर्देशों के अनुसार [SQLite एक्सटेंशन](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa) एक्सटेंशन इंस्टॉल करें
1. [code.visualstudio.com](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) पर नेविगेट करें और विजुअल स्टूडियो कोड इंस्टॉल करने के लिए निर्देशों का पालन करें
1. मार्केटप्लेस पेज पर दिए निर्देशों के अनुसार [SQLite एक्सटेंशन](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) एक्सटेंशन इंस्टॉल करें
### डेटाबेस डाउनलोड करें और खोलें
@ -25,7 +25,7 @@
एक बार खुलने के बाद, नई क्वेरी विंडो का उपयोग डेटाबेस के विरुद्ध SQL कथन चलाने के लिए किया जा सकता है। डेटाबेस के विरुद्ध क्वेरी चलाने के लिए आप **Ctl-Shift-Q** (या मैक पर **Cmd-Shift-Q**) कमांड का उपयोग कर सकते हैं।
> [!नोट] SQLite एक्सटेंशन के बारे में अधिक जानकारी के लिए, आप [दस्तावेज़ीकरण](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-40229-cxa) से परामर्श कर सकते हैं।
> [!नोट] SQLite एक्सटेंशन के बारे में अधिक जानकारी के लिए, आप [दस्तावेज़ीकरण](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) से परामर्श कर सकते हैं।
@ -16,7 +16,7 @@ Data processing can be programmed in any programming language, but there are cer
In this lesson, we will focus on using Python for simple data processing. We will assume basic familiarity with the language. If you want a deeper tour of Python, you can refer to one of the following resources:
* [Learn Python in a Fun Way with Turtle Graphics and Fractals](https://github.com/shwars/pycourse) - GitHub-based quick intro course into Python Programming
* [Take your First Steps with Python](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
* [Take your First Steps with Python](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
Data can come in many forms. In this lesson, we will consider three forms of data - **tabular data**, **text** and **images**.
@ -230,7 +230,7 @@ While data very often comes in tabular form, in some cases we need to deal with
In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is [CORD-19 Dataset](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).
A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-31812-dmitryso) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis.
A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis.
> **NOTE**: We do not provide a copy of the dataset as part of this repository. You may first need to download the [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) file from [this dataset on Kaggle](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge). Registration with Kaggle may be required. You may also download the dataset without registration [from here](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html), but it will include all full texts in addition to metadata file.
@ -242,15 +242,15 @@ Open [`notebook-papers.ipynb`](notebook-papers.ipynb) and read it from top to bo
Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:
* **Image Classification**, which can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso)
* **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso) model to detect some specific objects of interest.
* **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso).
* **Image Classification**, which can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)
* **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) model to detect some specific objects of interest.
* **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).
All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-31812-dmitryso), and thus can be easily incorporated into your data exploration workflow.
All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), and thus can be easily incorporated into your data exploration workflow.
Here are some examples of exploring data from Image data sources:
* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-31812-dmitryso) to build interpretable model.
* In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso) to extract emotions on people on photographs from events, in order to try to understand what makes people happy.
* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build interpretable model.
* In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to extract emotions on people on photographs from events, in order to try to understand what makes people happy.
## Conclusion
@ -271,7 +271,7 @@ Whether you already have structured or unstructured data, using Python you can p
**Learning Python**
* [Learn Python in a Fun Way with Turtle Graphics and Fractals](https://github.com/shwars/pycourse)
* [Take your First Steps with Python](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
* [Take your First Steps with Python](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
"In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is [CORD-19 Dataset](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).\n",
"\n",
"A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-31812-dmitryso) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis."
"A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis."
이 과정에서는 간단한 데이터 처리를 위해 파이썬을 사용하는 것에 초점을 맞출 것입니다. 사전에 파이썬에 익숙해질 필요가 있습니다. 파이썬에 대해 더 자세히 살펴보고 싶다면 다음 리소스 중 하나를 참조할 수 있습니다:
* [Turtle Graphics와 Fractal로 Python을 재미있게 배우기](https://github.com/shwars/pycourse) - GitHub 기반 Python 프로그래밍에 대한 빠른 소개 과정
* [Python으로 첫 걸음 내딛기](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) - [Microsoft 학습](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)으로 이동하기
* [Python으로 첫 걸음 내딛기](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) - [Microsoft 학습](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)으로 이동하기
데이터는 다양한 형태로 나타날 수 있습니다. 이 과정에서 우리는 세 가지 형태의 데이터를 고려할 것입니다. - **표로 나타낸 데이터(tabular data)**, **텍스트(text)** and **이미지(images)**.
@ -230,7 +230,7 @@ df = pd.read_csv('file.csv')
이 도전과제에서 우리는 COVID 팬데믹이라는 주제를 계속해서 다룰 것이며 해당 주제에 대한 과학 논문을 처리하는 데 집중할 것입니다. 메타데이터 및 초록과 함께 사용할 수 있는 COVID에 대한 7000개 이상의(작성 당시) 논문이 포함된 [CORD-19 데이터 세트](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)가 있습니다(이 중 약 절반에 대해 전체 텍스트도 제공됨).
[건강 인지 서비스를 위한 텍스트 분석](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-31812-dmitryso)를 사용하여 이 데이터 세트를 분석하는 전체 예는 이 블로그 게시물에 설명되어 있습니다. 우리는 이 분석의 단순화된 버전에 대해 논의할 것입니다.
[건강 인지 서비스를 위한 텍스트 분석](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum)를 사용하여 이 데이터 세트를 분석하는 전체 예는 이 블로그 게시물에 설명되어 있습니다. 우리는 이 분석의 단순화된 버전에 대해 논의할 것입니다.
> **주의**: 우리는 더이상 데이터 세트의 복사본을 이 리포지토리의 일부로 제공하지 않습니다. 먼저 [Kaggle의 데이터세트](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)에서 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 파일을 다운로드해야 할 수도 있습니다. Kaggle에 가입해야 할 수 있습니다. [여기](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html)에서 등록 없이 데이터 세트를 다운로드할 수도 있지만 여기에는 메타데이터 파일 외에 모든 전체 텍스트가 포함됩니다.
@ -242,15 +242,15 @@ df = pd.read_csv('file.csv')
최근에는 이미지를 이해할 수 있는 매우 강력한 AI 모델이 개발되었습니다. 사전에 훈련된 신경망이나 클라우드 서비스를 사용하여 해결할 수 있는 작업이 많이 있습니다. 몇 가지 예는 다음과 같습니다:
* **이미지 분류(Image Classification)** 는 이미지를 미리 정의된 클래스 중 하나로 분류하는 데 도움이 됩니다. [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso)과 같은 서비스를 사용하여 자신의 이미지 분류기를 쉽게 훈련할 수 있습니다.
* **물체 검출** 은 이미지에서 다른 물체를 감지합니다. [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso)과 같은 서비스는 여러 일반 개체를 감지할 수 있으며 [커스텀 비전(Custom Vision)](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso) 모델을 훈련하여 관심 있는 특정 개체를 감지할 수 있습니다.
* **얼굴 인식** 은 연령, 성별 및 감정 감지를 포함합니다. 이것은 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso)를 통해 수행할 수 있습니다.
* **이미지 분류(Image Classification)** 는 이미지를 미리 정의된 클래스 중 하나로 분류하는 데 도움이 됩니다. [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)과 같은 서비스를 사용하여 자신의 이미지 분류기를 쉽게 훈련할 수 있습니다.
* **물체 검출** 은 이미지에서 다른 물체를 감지합니다. [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum)과 같은 서비스는 여러 일반 개체를 감지할 수 있으며 [커스텀 비전(Custom Vision)](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 모델을 훈련하여 관심 있는 특정 개체를 감지할 수 있습니다.
* **얼굴 인식** 은 연령, 성별 및 감정 감지를 포함합니다. 이것은 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum)를 통해 수행할 수 있습니다.
이러한 모든 클라우드 서비스는 [Python SDK](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-31812-dmitryso)를 사용하여 호출할 수 있으므로, 데이터 탐색 워크플로에 쉽게 통합할 수 있습니다.
이러한 모든 클라우드 서비스는 [Python SDK](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum)를 사용하여 호출할 수 있으므로, 데이터 탐색 워크플로에 쉽게 통합할 수 있습니다.
다음은 이미지 데이터 소스에서 데이터를 탐색하는 몇 가지 예입니다:
* 블로그 게시물 중 [코딩 없이 데이터 과학을 배우는 방법](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/)에서 우리는 인스타그램 사진을 살펴보고 사람들이 사진에 더 많은 좋아요를 주는 이유를 이해하려고 합니다. 먼저 [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso)을 사용하여 사진에서 최대한 많은 정보를 추출한 다음 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-31812-dmitryso)을 사용하여 해석 가능한 모델을 빌드합니다.
* [얼굴 연구 워크숍(Facial Studies Workshop)](https://github.com/CloudAdvocacy/FaceStudies)에서는 사람들을 행복하게 만드는 요소를 이해하고자, 이벤트에서 사진에 있는 사람들의 감정을 추출하기 위해 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso)를 사용합니다.
* 블로그 게시물 중 [코딩 없이 데이터 과학을 배우는 방법](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/)에서 우리는 인스타그램 사진을 살펴보고 사람들이 사진에 더 많은 좋아요를 주는 이유를 이해하려고 합니다. 먼저 [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum)을 사용하여 사진에서 최대한 많은 정보를 추출한 다음 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum)을 사용하여 해석 가능한 모델을 빌드합니다.
* [얼굴 연구 워크숍(Facial Studies Workshop)](https://github.com/CloudAdvocacy/FaceStudies)에서는 사람들을 행복하게 만드는 요소를 이해하고자, 이벤트에서 사진에 있는 사람들의 감정을 추출하기 위해 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum)를 사용합니다.
## 결론
@ -273,7 +273,7 @@ df = pd.read_csv('file.csv')
**Python 학습**
* [거북이 그래픽과 도형으로 재미있는 방식으로 파이썬 배우기(Learn Python in a Fun Way with Turtle Graphics and Fractals)](https://github.com/shwars/pycourse)
* [파이썬으로 첫걸음(Take your First Steps with Python)](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso): 관련 강의 [Microsoft 강의](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
* [파이썬으로 첫걸음(Take your First Steps with Python)](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum): 관련 강의 [Microsoft 강의](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
@ -68,16 +68,16 @@ The steps necessary to create this project are as follows:
* Create an output sink and specify the job output
* Start the job
To view the full process, check out the [documentation](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid=AID30411099).
To view the full process, check out the [documentation](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099).
### Scientific papers analysis
Let’s take another example of a project created by [Dmitry Soshnikov](http://soshnikov.com), one of the authors of this curriculum.
Dmitry created a tool that analyses COVID papers. By reviewing this project, you will see how you can create a tool that extracts knowledge from scientific papers, gains insights and helps researchers navigate through large collections of papers in an efficient way.
Let's see the different steps used for this:
* Extracting and pre-processing information with [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
* Using [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) to parallelize the processing
* Storing and querying information with [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
* Extracting and pre-processing information with [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
* Using [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) to parallelize the processing
* Storing and querying information with [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
* Create an interactive dashboard for data exploration and visualization using Power BI
To see the full process, visit [Dmitry’s blog](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/).
पूरी प्रक्रिया देखने के लिए [प्रलेखन](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid=AID30411099) देखें।
पूरी प्रक्रिया देखने के लिए [प्रलेखन](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099) देखें।
### वैज्ञानिक कागजात विश्लेषण
आइए इस पाठ्यक्रम के लेखकों में से एक, [दिमित्री सोशनिकोव](http://soshnikov.com) द्वारा बनाई गई परियोजना का एक और उदाहरण लें।
@ -76,9 +76,9 @@
दिमित्री ने एक टूल बनाया जो कोविड पेपर्स का विश्लेषण करता है। इस परियोजना की समीक्षा करके, आप देखेंगे कि आप एक उपकरण कैसे बना सकते हैं जो वैज्ञानिक पत्रों से ज्ञान प्राप्त करता है, अंतर्दृष्टि प्राप्त करता है और शोधकर्ताओं को एक कुशल तरीके से कागजात के बड़े संग्रह के माध्यम से नेविगेट करने में मदद करता है।
आइए इसके लिए उपयोग किए जाने वाले विभिन्न चरणों को देखें:
* [टेक्स्ट एनालिटिक्स फॉर हेल्थ](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109) के साथ जानकारी निकालना और प्री-प्रोसेस करना
* प्रसंस्करण को समानांतर रखने के लिए [अज़ूरएमएल](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) का उपयोग करना
* [कॉसमॉस डीबी](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-40229-cxa&ocid=AID3041109) के साथ जानकारी संग्रहीत करना और क्वेरी करना
* [टेक्स्ट एनालिटिक्स फॉर हेल्थ](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) के साथ जानकारी निकालना और प्री-प्रोसेस करना
* प्रसंस्करण को समानांतर रखने के लिए [अज़ूरएमएल](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) का उपयोग करना
* [कॉसमॉस डीबी](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) के साथ जानकारी संग्रहीत करना और क्वेरी करना
* पावर बीआई का उपयोग करके डेटा अन्वेषण और विज़ुअलाइज़ेशन के लिए एक इंटरैक्टिव डैशबोर्ड बनाना
पूरी प्रक्रिया देखने के लिए [दिमित्री के ब्लॉग](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/) पर जाएँ।
전체 프로세스를 보려면 [문서](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid)를 확인하세요. =AID30411099).
전체 프로세스를 보려면 [문서](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid)를 확인하세요. =AID30411099).
### 과학 논문 분석
이 커리큘럼의 저자 중 한 명인 [Dmitry Soshnikov](http://soshnikov.com)가 만든 프로젝트의 또 다른 예를 들어보겠습니다.
Dmitry는 COVID 논문을 분석하는 도구를 만들었습니다. 이 프로젝트를 검토하면 과학 논문에서 지식을 추출하고 통찰력을 얻으며 연구자가 효율적인 방식으로 방대한 논문 컬렉션을 탐색하는 데 도움이 되는 도구를 만드는 방법을 알 수 있습니다.
이를 위해 사용된 다양한 단계를 살펴보겠습니다.
* [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109)로 정보 추출 및 전처리
* [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109)을 사용하여 처리 병렬화
* [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-40229-cxa&ocid=AID3041109)로 정보 저장 및 조회
* [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)로 정보 추출 및 전처리
* [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)을 사용하여 처리 병렬화
* [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)로 정보 저장 및 조회
* Power BI를 사용하여 데이터 탐색 및 시각화를 위한 대화형 대시보드 만들기
전체 과정을 보려면 [Dmitry의 블로그](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)를 방문하세요.
पूरा प्रक्रिया हेर्नको लागी [प्रलेखन](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid=AID30411099) ।
पूरा प्रक्रिया हेर्नको लागी [प्रलेखन](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099) ।
### वैज्ञानिक कागजात विश्लेषण
आउनुहोस् यस पाठ्यक्रमको लेखहरुमध्य एक, [दिमित्री सोशनिकोव](http://soshnikov.com) द्वारा बनाईएको परियोजनाको एउटा उदाहरण हेरौ।
@ -77,9 +77,9 @@
दिमित्रीले एउटा टूल बनाउनुभयो जो कोविड पेपर्सलाई विश्लेषण गर्छ । यस परियोजनाको समीक्षा गरेेर, तपाई देख्नसक्नुहुनेछ कि तपाई एक उपकरण कसरी बनाउन सक्नुहुनेछ जसले वैज्ञानिक पत्रबाट ज्ञान प्राप्त गर्ने छ, अंतर्दृष्टि प्राप्त गर्छ र शोधकर्ताहरुलाई एक कुशल तरीकाबाट कागजातको संग्रहको माध्यमबाट नेविगेट गर्न मदत गर्छ।
आउनुहोस् यसको लागि उपयोग गरिने विभिन्न चरणहरुलाई हेरौः
* [टेक्स्ट एनालिटिक्स फॉर हेल्थ](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109) को साथ जानकारी निकाल्न र प्री-प्रोसेस गर्न
* प्रसंस्करणलाई समानांतर राख्नका लागि [अज़ूरएमएल](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) को उपयोग गर्ने
* [कॉसमॉस डीबी](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-40229-cxa&ocid=AID3041109) को साथ जानकारी संग्रहीत गर्न र क्वेरी गर्न
* [टेक्स्ट एनालिटिक्स फॉर हेल्थ](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) को साथ जानकारी निकाल्न र प्री-प्रोसेस गर्न
* प्रसंस्करणलाई समानांतर राख्नका लागि [अज़ूरएमएल](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) को उपयोग गर्ने
* [कॉसमॉस डीबी](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) को साथ जानकारी संग्रहीत गर्न र क्वेरी गर्न
* पावर बीआईको उपयोग गरेर डेटा अन्वेषण र विज़ुअलाइज़ेशनका लागि एक इंटरैक्टिव डैशबोर्ड बनाउन
पूरा प्रक्रिया हेर्नका लागि [दिमित्री के ब्लॉग](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)
The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life.
Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models. These tasks are time consuming, and often make inefficient use of expensive compute hardware.
[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
Azure ML provides all the tools developers and data scientists need for their machine learning workflows. These include:
@ -89,7 +89,7 @@ Once you have the dataset, we can start the project in Azure.
## 2. Low code/No code training of a model in Azure ML Studio
### 2.1 Create an Azure ML workspace
To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
It is recommended to use the most up-to-date browser that's compatible with your operating system. The following browsers are supported:
@ -217,7 +217,7 @@ Great! Now that the dataset is in place and the compute cluster is created, we c
### 2.4 Low code/No Code training with AutoML
Traditional machine learning model development is resource-intensive, requires significant domain knowledge and time to produce and compare dozens of models.
Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. It reduces the time it takes to get production-ready ML models, with great ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. It reduces the time it takes to get production-ready ML models, with great ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
1. In the [Azure ML workspace](https://ml.azure.com/) that we created earlier click on "Automated ML" in the left menu and select the dataset you just uploaded. Click Next.
@ -331,7 +331,7 @@ Look closely at the model explanations and details that AutoML generated for the
In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk in a Low code/No code fashion in the cloud. If you have not done it yet, dive deeper into the model explanations that AutoML generated for the top models and try to understand why the best model is better than others.
You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
Azure 클라우드 플랫폼은 새로운 솔루션에 생명을 불어넣는 데 도움이 되도록 설계된 200개 이상의 제품 및 클라우드 서비스입니다.
데이터 사이언티스트는 데이터를 탐색하고 전처리하며 다양한 유형의 모델 학습 알고리즘을 시도하여 정확한 모델을 생성하는 데 많은 노력을 기울입니다. 이러한 작업은 시간이 많이 걸리고 종종 값비싼 컴퓨팅 하드웨어를 비효율적으로 사용합니다.
[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109)은 클라우드 기반 Azure에서 기계 학습 솔루션을 구축하고 운영하기 위한 플랫폼입니다. 여기에는 데이터 사이언티스트가 데이터를 준비하고, 모델을 훈련하고, 예측 서비스를 게시하고, 사용량을 모니터링하는 데 도움이 되는 다양한 기능이 포함되어 있습니다. 가장 중요한 것은 훈련 모델과 관련된 많은 시간 소모적인 작업을 자동화하여 효율성을 높이는 데 도움이 된다는 것입니다. 또한 효과적으로 확장되는 클라우드 기반 컴퓨팅 리소스를 사용하여 실제로 사용할 때만 비용을 발생시키면서 대량의 데이터를 처리할 수 있습니다.
[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)은 클라우드 기반 Azure에서 기계 학습 솔루션을 구축하고 운영하기 위한 플랫폼입니다. 여기에는 데이터 사이언티스트가 데이터를 준비하고, 모델을 훈련하고, 예측 서비스를 게시하고, 사용량을 모니터링하는 데 도움이 되는 다양한 기능이 포함되어 있습니다. 가장 중요한 것은 훈련 모델과 관련된 많은 시간 소모적인 작업을 자동화하여 효율성을 높이는 데 도움이 된다는 것입니다. 또한 효과적으로 확장되는 클라우드 기반 컴퓨팅 리소스를 사용하여 실제로 사용할 때만 비용을 발생시키면서 대량의 데이터를 처리할 수 있습니다.
Azure ML은 개발자와 데이터 사이언티스트가 기계 학습 워크플로에 필요한 모든 도구를 제공합니다. 여기에는 다음이 포함됩니다.
@ -89,7 +89,7 @@ Kaggle은 이 프로젝트에 사용할 [Heart Failure dataset](https://www.kagg
## 2. Azure ML Studio에서 모델의 로우 코드/노 코드 학습(Training)
### 2.1 Azure ML 워크스페이스 만들기
Azure ML에서 모델을 학습시키려면 먼저 Azure ML 워크스페이스를 만들어야 합니다. 워크스페이스는 Azure Machine Learning의 최상위 리소스로, Azure Machine Learning을 사용할 때 만드는 모든 아티팩트를 작업할 수 있는 중앙 집중식 장소를 제공합니다. 작업 공간은 로그, 메트릭, 출력 및 스크립트의 스냅샷을 포함하여 모든 훈련 실행의 기록을 유지합니다. 이 정보를 사용하여 최상의 모델을 생성하는 훈련 실행을 결정합니다. [자세히 알아보기](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
Azure ML에서 모델을 학습시키려면 먼저 Azure ML 워크스페이스를 만들어야 합니다. 워크스페이스는 Azure Machine Learning의 최상위 리소스로, Azure Machine Learning을 사용할 때 만드는 모든 아티팩트를 작업할 수 있는 중앙 집중식 장소를 제공합니다. 작업 공간은 로그, 메트릭, 출력 및 스크립트의 스냅샷을 포함하여 모든 훈련 실행의 기록을 유지합니다. 이 정보를 사용하여 최상의 모델을 생성하는 훈련 실행을 결정합니다. [자세히 알아보기](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
운영 체제와 호환되는 최신 브라우저를 사용하는 것이 좋습니다. 다음 브라우저가 지원됩니다.
@ -217,7 +217,7 @@ CPU와 GPU 아키텍처의 주요 차이점은 CPU가 광범위한 작업을 빠
### 2.4 AutoML을 사용한 로우 코드/노 코드 학습
전통적인 기계 학습 모델 개발은 리소스 집약적이며 수십 개의 모델을 생성하고 비교하는 데 상당한 도메인 지식과 시간이 필요합니다.
AutoML(자동화된 기계 학습)은 기계 학습 모델 개발의 시간 소모적이고 반복적인 작업을 자동화하는 프로세스입니다. 이를 통해 데이터 사이언티스트, 분석가 및 개발자는 모델 품질을 유지하면서 높은 확장성, 효율성 및 생산성을 갖춘 ML 모델을 구축할 수 있습니다. 프로덕션 준비 ML 모델을 매우 쉽고 효율적으로 얻는 데 걸리는 시간을 줄입니다. [자세히 알아보기](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
AutoML(자동화된 기계 학습)은 기계 학습 모델 개발의 시간 소모적이고 반복적인 작업을 자동화하는 프로세스입니다. 이를 통해 데이터 사이언티스트, 분석가 및 개발자는 모델 품질을 유지하면서 높은 확장성, 효율성 및 생산성을 갖춘 ML 모델을 구축할 수 있습니다. 프로덕션 준비 ML 모델을 매우 쉽고 효율적으로 얻는 데 걸리는 시간을 줄입니다. [자세히 알아보기](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
1. 앞서 생성한 [Azure ML 워크스페이스](https://ml.azure.com/)에서 왼쪽 메뉴의 "Automated ML"을 클릭하고 방금 업로드한 데이터 셋을 선택합니다. 다음을 클릭합니다.
@ -331,7 +331,7 @@ AutoML이 상위 모델에 대해 생성한 모델 설명 및 세부정보를
이 강의에서는 클라우드에서 로우 코드/노 코드 방식으로 심부전 위험을 예측하기 위해 모델을 훈련, 배포 및 사용하는 방법을 배웠습니다. 아직 수행하지 않았다면 AutoML이 상위 모델에 대해 생성한 모델 설명을 더 자세히 살펴보고 최고의 모델이 다른 모델보다 더 나은 이유를 이해하려고 합니다.
로우 코드/노 코드 Auto ML 에 대해 더 알아보고 싶다면 이 [문서](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)를 읽어보세요.
로우 코드/노 코드 Auto ML 에 대해 더 알아보고 싶다면 이 [문서](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)를 읽어보세요.
Azure ML 플랫폼을 사용하여 로우 코드/노 코드 방식으로 모델을 학습, 배포 및 사용하는 방법을 보았습니다. 이제 다른 모델을 훈련하고 배포하고 소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109)에서 데이터셋을 찾을 수 있습니다.
Azure ML 플랫폼을 사용하여 로우 코드/노 코드 방식으로 모델을 학습, 배포 및 사용하는 방법을 보았습니다. 이제 다른 모델을 훈련하고 배포하고 소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)에서 데이터셋을 찾을 수 있습니다.
- Use automated machine learning, which accepts configuration parameters and training data. It automatically iterates through algorithms and hyperparameter settings to find the best model for running predictions.
- Deploy web services to convert your trained models into RESTful services that can be consumed in any application.
[Learn more about the Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
[Learn more about the Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
In the [previous lesson](../18-Low-Code/README.md), we saw how to train, deploy and consume a model in a Low code/No code fashion. We used the Heart Failure dataset to generate and Heart failure prediction model. In this lesson, we are going to do the exact same thing but using the Azure Machine Learning SDK.
@ -97,7 +97,7 @@ Now that we have a Notebook, we can start training the model with Azure ML SDK.
### 2.5 Training a model
First of all, if you ever have a doubt, refer to the [Azure ML SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109). It contains all the necessary information to understand the modules we are going to see in this lesson.
First of all, if you ever have a doubt, refer to the [Azure ML SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). It contains all the necessary information to understand the modules we are going to see in this lesson.
#### 2.5.1 Setup Workspace, experiment, compute cluster and dataset
@ -145,7 +145,7 @@ df.describe()
```
#### 2.5.2 AutoML Configuration and training
To set the AutoML configuration, use the [AutoMLConfig class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
To set the AutoML configuration, use the [AutoMLConfig class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
As described in the doc, there are a lot of parameters with which you can play with. For this project, we will use the following parameters:
The `remote_run` an object of type [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-40229-cxa&ocid=AID3041109). This object contains the method `get_output()` which returns the best run and the corresponding fitted model.
The `remote_run` an object of type [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109). This object contains the method `get_output()` which returns the best run and the corresponding fitted model.
```python
best_run, fitted_model = remote_run.get_output()
```
You can see the parameters used for the best model by just printing the fitted_model and see the properties of the best model by using the [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-40229-cxa&ocid=AID3041109) method.
You can see the parameters used for the best model by just printing the fitted_model and see the properties of the best model by using the [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
```python
best_run.get_properties()
```
Now register the model with the [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-40229-cxa&ocid=AID3041109) method.
Now register the model with the [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
```python
model_name = best_run.properties['model_name']
script_file_name = 'inference/score.py'
@ -223,7 +223,7 @@ model = best_run.register_model(model_name = model_name,
Once the best model is saved, we can deploy it with the [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) class. InferenceConfig represents the configuration settings for a custom environment used for deployment. The [AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) class represents a machine learning model deployed as a web service endpoint on Azure Container Instances. A deployed service is created from a model, script, and associated files. The resulting web service is a load-balanced, HTTP endpoint with a REST API. You can send data to this API and receive the prediction returned by the model.
The model is deployed using the [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-40229-cxa&ocid=AID3041109) method.
The model is deployed using the [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) method.
```python
from azureml.core.model import InferenceConfig, Model
@ -286,13 +286,13 @@ Congratulations! You just consumed the model deployed and trained on Azure ML wi
There are many other things you can do through the SDK, unfortunately, we can not view them all in this lesson. But good news, learning how to skim through the SDK documentation can take you a long way on your own. Have a look at the Azure ML SDK documentation and find the `Pipeline` class that allows you to create pipelines. A Pipeline is a collection of steps which can be executed as a workflow.
**HINT:** Go to the [SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109) and type keywords in the search bar like "Pipeline". You should have the `azureml.pipeline.core.Pipeline` class in the search results.
**HINT:** Go to the [SDK documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) and type keywords in the search bar like "Pipeline". You should have the `azureml.pipeline.core.Pipeline` class in the search results.
In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk with the Azure ML SDK in the cloud. Check this [documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109) for further information about the Azure ML SDK. Try to create your own model with the Azure ML SDK.
In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk with the Azure ML SDK in the cloud. Check this [documentation](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) for further information about the Azure ML SDK. Try to create your own model with the Azure ML SDK.
We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109).
- 구성 매개변수 및 교육 데이터를 허용하는 자동화된 기계 학습을 사용합니다. 알고리즘과 하이퍼파라미터 설정을 자동으로 반복하여 예측 실행에 가장 적합한 모델을 찾습니다.
- 웹 서비스를 배포하여 훈련된 모델을 모든 애플리케이션에서 사용할 수 있는 RESTful 서비스로 변환합니다.
[Azure Machine Learning SDK에 대해 자세히 알아보기](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
[Azure Machine Learning SDK에 대해 자세히 알아보기](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
[이전 강의](../../18-Low-Code/translations/README.ko.md)에서 Low code/No code 방식으로 모델을 훈련, 배포 및 소비하는 방법을 살펴보았습니다. 심부전 데이터셋을 사용하여 심부전 예측 모델을 생성했습니다. 이 단원에서는 Azure Machine Learning SDK를 사용하여 똑같은 작업을 수행할 것입니다.
먼저 궁금한 점이 있으시면 [Azure ML SDK 설명서](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)을 참고할 수 있습니다. 여기에는 이 단원에서 보게 될 모듈을 이해하는 데 필요한 모든 정보가 포함되어 있습니다.
먼저 궁금한 점이 있으시면 [Azure ML SDK 설명서](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)을 참고할 수 있습니다. 여기에는 이 단원에서 보게 될 모듈을 이해하는 데 필요한 모든 정보가 포함되어 있습니다.
#### 2.5.1 작업 공간, 실험, 컴퓨팅 클러스터 및 데이터셋 설정
@ -145,7 +145,7 @@ df.describe()
```
#### 2.5.2 AutoML 구성 및 교육
AutoML 구성을 설정하려면 [AutoMLConfig 클래스](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-40229-cxa&ocid=AID3041109)를 사용하세요.
AutoML 구성을 설정하려면 [AutoMLConfig 클래스](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)를 사용하세요.
문서에 설명된 대로 가지고 놀 수 있는 많은 매개변수가 있습니다. 이 프로젝트에서는 다음 매개변수를 사용합니다.
[AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-40229-cxa&ocid=AID3041109)타입 중 하나인 `remote_run` 객체. 이 객체에는 최상의 실행과 해당하는 적합 모델을 반환하는 `get_output()` 메서드가 포함되어 있습니다.
[AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)타입 중 하나인 `remote_run` 객체. 이 객체에는 최상의 실행과 해당하는 적합 모델을 반환하는 `get_output()` 메서드가 포함되어 있습니다.
```python
best_run, fitted_model = remote_run.get_output()
```
fit_model을 출력하기만 하면 최상의 모델에 사용된 매개변수를 볼 수 있고 [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-40229-cxa&ocid=AID3041109) 메소드를 사용하여 최상의 모델의 속성을 볼 수 있습니다.
fit_model을 출력하기만 하면 최상의 모델에 사용된 매개변수를 볼 수 있고 [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 메소드를 사용하여 최상의 모델의 속성을 볼 수 있습니다.
```python
best_run.get_properties()
```
이제 [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-40229-cxa&ocid=AID3041109) 방법을 사용해 모델을 등록해봅시다.
이제 [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 방법을 사용해 모델을 등록해봅시다.
```python
model_name = best_run.properties['model_name']
script_file_name = 'inference/score.py'
@ -223,7 +223,7 @@ model = best_run.register_model(model_name = model_name,
최상의 모델이 저장되면 [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) 클래스를 사용하여 배포할 수 있습니다. InferenceConfig는 배포에 사용되는 사용자 지정 환경에 대한 구성 설정을 나타냅니다. [AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) 클래스는 웹 서비스로 배포된 기계 학습 모델을 나타냅니다. Azure Container Instances의 엔드포인트. 배포된 서비스는 모델, 스크립트 및 관련 파일에서 생성됩니다. 결과 웹 서비스는 REST API가 있는 로드 밸런싱된 HTTP 엔드포인트입니다. 이 API로 데이터를 보내고 모델에서 반환된 예측을 받을 수 있습니다.
모델은 [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-40229-cxa&ocid=AID3041109) 방법을 사용하여 배포됩니다.
모델은 [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 방법을 사용하여 배포됩니다.
```python
from azureml.core.model import InferenceConfig, Model
@ -286,13 +286,13 @@ response
SDK를 통해 수행할 수 있는 다른 많은 작업이 있지만 불행히도 이 강의에서 모두 볼 수는 없습니다. 그러나 좋은 소식은 SDK 문서를 훑어보는 방법을 배우면 스스로 많은 시간을 할애할 수 있다는 것입니다. Azure ML SDK 설명서를 살펴보고 파이프라인을 만들 수 있는 'Pipeline' 클래스를 찾으세요. 파이프라인은 워크플로로 실행할 수 있는 단계 모음입니다.
**힌트:** [SDK 설명서](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109) 로 이동합니다. 검색창에 "파이프라인"과 같은 키워드를 입력합니다. 검색 결과에 `azureml.pipeline.core.Pipeline` 클래스가 있어야 합니다.
**힌트:** [SDK 설명서](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 로 이동합니다. 검색창에 "파이프라인"과 같은 키워드를 입력합니다. 검색 결과에 `azureml.pipeline.core.Pipeline` 클래스가 있어야 합니다.
## [강의 후 퀴즈](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/37)
## 복습 및 독학
이 단원에서는 클라우드에서 Azure ML SDK를 사용하여 심부전 위험을 예측하기 위해 모델을 학습, 배포 및 사용하는 방법을 배웠습니다. 자세한 내용은 이 [문서](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-40229-cxa&ocid=AID3041109)를 확인하세요. Azure ML SDK에 대해 Azure ML SDK를 사용하여 고유한 모델을 만들어 보세요.
이 단원에서는 클라우드에서 Azure ML SDK를 사용하여 심부전 위험을 예측하기 위해 모델을 학습, 배포 및 사용하는 방법을 배웠습니다. 자세한 내용은 이 [문서](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)를 확인하세요. Azure ML SDK에 대해 Azure ML SDK를 사용하여 고유한 모델을 만들어 보세요.
हमने देखा कि एज़्योर एमएल एसडीके के साथ एक मॉडल को प्रशिक्षित करने, तैनात करने और उपभोग करने के लिए एज़्योर एमएल प्लेटफॉर्म का उपयोग कैसे किया जाता है। अब कुछ डेटा के लिए चारों ओर देखें, जिसका उपयोग आप किसी अन्य मॉडल को प्रशिक्षित करने, उसे परिनियोजित करने और उसका उपभोग करने के लिए कर सकते हैं। आप [कागल](https://kaggle.com) और [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229 पर डेटासेट ढूंढ सकते हैं। -cxa&ocid=AID3041109)।
हमने देखा कि एज़्योर एमएल एसडीके के साथ एक मॉडल को प्रशिक्षित करने, तैनात करने और उपभोग करने के लिए एज़्योर एमएल प्लेटफॉर्म का उपयोग कैसे किया जाता है। अब कुछ डेटा के लिए चारों ओर देखें, जिसका उपयोग आप किसी अन्य मॉडल को प्रशिक्षित करने, उसे परिनियोजित करने और उसका उपभोग करने के लिए कर सकते हैं। आप [कागल](https://kaggle.com) और [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958 पर डेटासेट ढूंढ सकते हैं। -bethanycheum&ocid=AID3041109)।
Azure ML 플랫폼을 사용하여 Azure ML SDK로 모델을 학습, 배포 및 사용하는 방법을 살펴보았습니다. 이제 다른 모델을 학습하고 배포하고 소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109)에서 데이터 셋을 찾을 수 있습니다.
Azure ML 플랫폼을 사용하여 Azure ML SDK로 모델을 학습, 배포 및 사용하는 방법을 살펴보았습니다. 이제 다른 모델을 학습하고 배포하고 소비하는 데 사용할 수 있는 일부 데이터를 찾아보십시오. [Kaggle](https://kaggle.com) 및 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)에서 데이터 셋을 찾을 수 있습니다.
हामीले Azure ML SDK सँग मोडेललाई तालिम, डिप्लोय, र उपभोग गर्न Azure ML प्लेटफर्म कसरी प्रयोग गर्ने भनेर हेर्यौं। अब केहि डेटा को लागी वरिपरि हेर्नुहोस् जुन तपाईले अर्को मोडेललाई प्रशिक्षित गर्न, प्रयोग गर्न र उपभोग गर्न सक्नुहुन्छ। तपाईंले Kaggle र [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109) मा डाटासेट फेला पार्न सक्नुहुन्छ।
हामीले Azure ML SDK सँग मोडेललाई तालिम, डिप्लोय, र उपभोग गर्न Azure ML प्लेटफर्म कसरी प्रयोग गर्ने भनेर हेर्यौं। अब केहि डेटा को लागी वरिपरि हेर्नुहोस् जुन तपाईले अर्को मोडेललाई प्रशिक्षित गर्न, प्रयोग गर्न र उपभोग गर्न सक्नुहुन्छ। तपाईंले Kaggle र [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) मा डाटासेट फेला पार्न सक्नुहुन्छ।
@ -25,14 +25,14 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur
Get started with the following resources:
- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-13441-cxa) In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-13441-cxa) Join a global community of student ambassadors, this could be your way into Microsoft
- [Student Hub page](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Join a global community of student ambassadors, this could be your way into Microsoft
# Getting Started
> **Teachers**: we have [included some suggestions](for-teachers.md) on how to use this curriculum. We'd love your feedback [in our discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **[Students](https://aka.ms/student-page)**: to use this curriculum on your own, fork the entire repo and complete the exercises on your own, starting with a pre-lecture quiz. Then read the lecture and complete the rest of the activities. Try to create the projects by comprehending the lessons rather than copying the solution code; however, that code is available in the /solutions folders in each project-oriented lesson. Another idea would be to form a study group with friends and go through the content together. For further study, we recommend [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **[Students](https://aka.ms/student-page)**: to use this curriculum on your own, fork the entire repo and complete the exercises on your own, starting with a pre-lecture quiz. Then read the lecture and complete the rest of the activities. Try to create the projects by comprehending the lessons rather than copying the solution code; however, that code is available in the /solutions folders in each project-oriented lesson. Another idea would be to form a study group with friends and go through the content together. For further study, we recommend [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
> **معلمان**، ما در مورد نحوه استفاده از این برنامه درسی [برخی از پیشنهادات را درج کرده ایم](../for-teachers.md). بسیار خوشحال می شویم که بازخوردهای شما را در [انجمن بحث و گفت و گوی](https://github.com/microsoft/Data-Science-For-Beginners/discussions) خود داشته باشیم!
> **دانش آموزان**، اگر قصد دارید به تنهایی از این برنامه درسی استفاده کنید، کل مخزن را فورک کنید و تمرینات را خودتان به تنهایی انجام دهید. ابتدا با آزمون قبل از درس آغاز کنید، سپس درسنامه را خوانده و باقی فعالیت ها را تکمیل کنید. سعی کنید به جای کپی کردن کد راه حل، خودتان پروژه ها را با درک مفاهیم درسنامه ایجاد کنید. با این حال،کد راه حل در پوشه های /solutions داخل هر درس پروژه محور موجود می باشد. ایده دیگر تشکیل گروه مطالعه با دوستان است تا بتوانید مطالب را با هم مرور کنید، پیشنهاد ما [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa) می باشد.
> **دانش آموزان**، اگر قصد دارید به تنهایی از این برنامه درسی استفاده کنید، کل مخزن را فورک کنید و تمرینات را خودتان به تنهایی انجام دهید. ابتدا با آزمون قبل از درس آغاز کنید، سپس درسنامه را خوانده و باقی فعالیت ها را تکمیل کنید. سعی کنید به جای کپی کردن کد راه حل، خودتان پروژه ها را با درک مفاهیم درسنامه ایجاد کنید. با این حال،کد راه حل در پوشه های /solutions داخل هر درس پروژه محور موجود می باشد. ایده دیگر تشکیل گروه مطالعه با دوستان است تا بتوانید مطالب را با هم مرور کنید، پیشنهاد ما [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) می باشد.
@ -25,7 +25,7 @@ L'équipe Azure Cloud Advocates de Microsoft a le plaisir de vous offrir un curr
> **Enseignants**, nous avons [inclus des suggestions](../for-teachers.md) concernant la manière dont vous pouvez utiliser ce curriculum. Nous aimerions beaucoup lire vos feedbacks [dans notre forum de discussion](https://github.com/microsoft/Data-Science-For-Beginners/discussions) !
> **Etudiants**, pour suivre ce curriculum, la première chose à faire est de forker ce repository en entier, vous pourrez ensuite réaliser les exercices de votre côté, en commençant un quiz préalable, en lisant le contenu des cours, et en complétant le reste des activités. Essayez de créer les projets en intégrant bien les cours, plutôt qu'en copiant les solutions. Vous verrez que chaque cours orientée projet contient un dossier dossier /solutions dans lequel vous trouverez la solution des exercices. Vous pouvez aussi former un groupe d'apprentissage avec des amis et vous former ensemble. Pour poursuivre votre apprentissage, nous recommandons d'aller consulter [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **Etudiants**, pour suivre ce curriculum, la première chose à faire est de forker ce repository en entier, vous pourrez ensuite réaliser les exercices de votre côté, en commençant un quiz préalable, en lisant le contenu des cours, et en complétant le reste des activités. Essayez de créer les projets en intégrant bien les cours, plutôt qu'en copiant les solutions. Vous verrez que chaque cours orientée projet contient un dossier dossier /solutions dans lequel vous trouverez la solution des exercices. Vous pouvez aussi former un groupe d'apprentissage avec des amis et vous former ensemble. Pour poursuivre votre apprentissage, nous recommandons d'aller consulter [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
@ -26,7 +26,7 @@ Microsoft의 Azure Cloud Advocates는 데이터 과학에 관한 10주짜리 20
> **선생님들께**, 이 커리큘럼의 사용 방법에 대해 [일부 제안사항](for-teachers.md)이 있습니다. 이 [포럼에서](https://github.com/microsoft/Data-Science-For-Beginners/discussions) 의견을 주시면 감사하겠습니다.
> **학생분들께**, 스스로 이 커리큘럼을 활용하려면 강의 전 퀴즈부터 시작하여 강의 전 학습 과정을 읽고 나머지 과제를 완료하면 됩니다. 솔루션 코드를 복사하는 대신 레슨을 이해하여 프로젝트를 만들어 보십시오. 이 코드들의 답안은 각 프로젝트 지향 레슨에 있는 /solutions 폴더에서 찾을 수 있습니다. 또 다른 방법으로는 친구들과 함께 학습 내용을 살펴보는 스터디 그룹을 만드는 것입니다. 추가 학습은 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa)을 추천합니다.
> **학생분들께**, 스스로 이 커리큘럼을 활용하려면 강의 전 퀴즈부터 시작하여 강의 전 학습 과정을 읽고 나머지 과제를 완료하면 됩니다. 솔루션 코드를 복사하는 대신 레슨을 이해하여 프로젝트를 만들어 보십시오. 이 코드들의 답안은 각 프로젝트 지향 레슨에 있는 /solutions 폴더에서 찾을 수 있습니다. 또 다른 방법으로는 친구들과 함께 학습 내용을 살펴보는 스터디 그룹을 만드는 것입니다. 추가 학습은 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)을 추천합니다.
@ -15,7 +15,7 @@ Microsoft मा Azure Cloud अधिवक्ताहरु एक १०-ह
> **शिक्षकहरु**, हामीले कसरी यो पाठ्यक्रम को उपयोग गर्न [केहि सुझावहरु ](for-teachers.md) मा समावेस गरेका छौ । हामी तपाइँको प्रतिक्रिया [हाम्रो Discussion Forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions) मा सुन्न आतुर छौ !
> **विद्यार्थी**, यो पाठ्यक्रम आफ्नै शैलिमा प्रयोग गर्नका लागी यो Repo लाई fork गर्नुहोस् र एक पूर्व व्याख्यान प्रश्नोत्तरी संग शुरू गरी त्यसपछि गतिविधिहरु को बाकी पूरा लेक्चर पढी अभ्यास पूरा गर्नुहोस् । समाधान कोड प्रतिलिपि गर्नुको सट्टा पाठ बुझेर परियोजनाहरु बनाउन को लागी प्रयास गर्नुहोस्; जे होस् कि कोड प्रत्येक परियोजना उन्मुख पाठ मा /solution फोल्डरहरु मा उपलब्ध छ। अर्को विचार साथीहरु संग एक साथ सामग्री को माध्यम बाट जाने संग एक अध्ययन समूह गठन गर्न को लागी हुनेछ। थप अध्ययन को लागी, हामी [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa)सिफारिश गर्दछौं ।
> **विद्यार्थी**, यो पाठ्यक्रम आफ्नै शैलिमा प्रयोग गर्नका लागी यो Repo लाई fork गर्नुहोस् र एक पूर्व व्याख्यान प्रश्नोत्तरी संग शुरू गरी त्यसपछि गतिविधिहरु को बाकी पूरा लेक्चर पढी अभ्यास पूरा गर्नुहोस् । समाधान कोड प्रतिलिपि गर्नुको सट्टा पाठ बुझेर परियोजनाहरु बनाउन को लागी प्रयास गर्नुहोस्; जे होस् कि कोड प्रत्येक परियोजना उन्मुख पाठ मा /solution फोल्डरहरु मा उपलब्ध छ। अर्को विचार साथीहरु संग एक साथ सामग्री को माध्यम बाट जाने संग एक अध्ययन समूह गठन गर्न को लागी हुनेछ। थप अध्ययन को लागी, हामी [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)सिफारिश गर्दछौं ।
@ -15,7 +15,7 @@ Microsoft मा Azure Cloud अधिवक्ताहरु एक १०-ह
> **शिक्षकहरु**, हामीले कसरी यो पाठ्यक्रम को उपयोग गर्न [केहि सुझावहरु ](for-teachers.md) मा समावेस गरेका छौ । हामी तपाइँको प्रतिक्रिया [हाम्रो Discussion Forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions) मा सुन्न आतुर छौ !
> **विद्यार्थी**, यो पाठ्यक्रम आफ्नै शैलिमा प्रयोग गर्नका लागी यो Repo लाई fork गर्नुहोस् र एक पूर्व व्याख्यान प्रश्नोत्तरी संग शुरू गरी त्यसपछि गतिविधिहरु को बाकी पूरा लेक्चर पढी अभ्यास पूरा गर्नुहोस् । समाधान कोड प्रतिलिपि गर्नुको सट्टा पाठ बुझेर परियोजनाहरु बनाउन को लागी प्रयास गर्नुहोस्; जे होस् कि कोड प्रत्येक परियोजना उन्मुख पाठ मा /solution फोल्डरहरु मा उपलब्ध छ। अर्को विचार साथीहरु संग एक साथ सामग्री को माध्यम बाट जाने संग एक अध्ययन समूह गठन गर्न को लागी हुनेछ। थप अध्ययन को लागी, हामी [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa)सिफारिश गर्दछौं ।
> **विद्यार्थी**, यो पाठ्यक्रम आफ्नै शैलिमा प्रयोग गर्नका लागी यो Repo लाई fork गर्नुहोस् र एक पूर्व व्याख्यान प्रश्नोत्तरी संग शुरू गरी त्यसपछि गतिविधिहरु को बाकी पूरा लेक्चर पढी अभ्यास पूरा गर्नुहोस् । समाधान कोड प्रतिलिपि गर्नुको सट्टा पाठ बुझेर परियोजनाहरु बनाउन को लागी प्रयास गर्नुहोस्; जे होस् कि कोड प्रत्येक परियोजना उन्मुख पाठ मा /solution फोल्डरहरु मा उपलब्ध छ। अर्को विचार साथीहरु संग एक साथ सामग्री को माध्यम बाट जाने संग एक अध्ययन समूह गठन गर्न को लागी हुनेछ। थप अध्ययन को लागी, हामी [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)सिफारिश गर्दछौं ।
@ -28,7 +28,7 @@ Met groot genoegen bieden Azure Cloud Advocates bij Microsoft dit curriculum van
> **Leerkrachten**: we hebben [suggesties bijgevoegd](for-teachers.md) over het gebruik van dit curriculum. We staan open voor uw feedback [in ons discussie forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **Studenten, leerlingen**: "fork" om dit lesmateriaal te gebruiken de gehele folder, en werk op eigen kracht door de opdrachten. Start steeds met de quiz vooraf. Lees dan de lezing en volg de rest van de opdrachten. Probeer de projecten te voltooien zonder de oplossing een-op-een te kopiëren; maar weet dat de oplossing in de /solutions folder te vinden is. Overweeg een studie groep te vormen en samen door het lesmateriaal te gaan. Wil je nog meer leren? Ga dan naar [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **Studenten, leerlingen**: "fork" om dit lesmateriaal te gebruiken de gehele folder, en werk op eigen kracht door de opdrachten. Start steeds met de quiz vooraf. Lees dan de lezing en volg de rest van de opdrachten. Probeer de projecten te voltooien zonder de oplossing een-op-een te kopiëren; maar weet dat de oplossing in de /solutions folder te vinden is. Overweeg een studie groep te vormen en samen door het lesmateriaal te gaan. Wil je nog meer leren? Ga dan naar [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
@ -24,14 +24,14 @@ Consultores da Azure Cloud na Microsoft estão felizes em oferecer um currículo
Comece com os seguintes recursos:
- [Página do Hub de Alunos](https://docs.microsoft.com/pt-br/learn/student-hub/?WT.mc_id=academic-13441-cxa) Nessa página, você irá encontrar recursos de iniciantes, pacotes estudantis e até mesmo modos de conseguir certificados de graça. Essa é uma página que você vai querer favoritar e checar de tempos em tempos pois nós mudamos o conteúdo pelo menos mensalmente.
- [Página do Hub de Alunos](https://docs.microsoft.com/pt-br/learn/student-hub/?WT.mc_id=academic-77958-bethanycheum) Nessa página, você irá encontrar recursos de iniciantes, pacotes estudantis e até mesmo modos de conseguir certificados de graça. Essa é uma página que você vai querer favoritar e checar de tempos em tempos pois nós mudamos o conteúdo pelo menos mensalmente.
- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com/pt-BR) Junte-se a uma comunidade global de embaixadores estudantis, esse pode ser o seu caminho à Microsoft.
# Primeiros Passos
> **Professores**, nós [incluímos algumas sugestões](for-teachers.md) em como usar esse currículo. Nós adoraríamos ouvir o seu feedback [no nosso fórum de discussão](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **Estudantes**, para usar esse currículo por conta própria, dê fork nesse repositório, complete os exercícios por sua conta, começando com um quiz pré aula, então leia a aula completando o resto das atividades. Tente criar os projetos compreendendo as aulas ao invés de copiar o código da solução; no entanto o código está disponível na pasta /solutions em cada aula baseada em projeto. Outra ideia seria formar um grupo de estudo com seus amigos e ler o conteúdo juntos. Para mais estudos, nós recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **Estudantes**, para usar esse currículo por conta própria, dê fork nesse repositório, complete os exercícios por sua conta, começando com um quiz pré aula, então leia a aula completando o resto das atividades. Tente criar os projetos compreendendo as aulas ao invés de copiar o código da solução; no entanto o código está disponível na pasta /solutions em cada aula baseada em projeto. Outra ideia seria formar um grupo de estudo com seus amigos e ler o conteúdo juntos. Para mais estudos, nós recomendamos [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
@ -27,7 +27,7 @@ Os promotores da Azure Cloud na Microsoft estam entusiasmados por oferecer 10 se
> **Para Professores**: nós [incluímos algumas sugestões](for-teachers.md) em como usar este curso. Adorávamos ou vir a vossa opínião [no nosso paínel de discussões](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **Para Estudantes**: para utilizares este cursão por conta própria, faz fork deste repositório e completa cada um dos exercícios, começando sempre pelo quiz pré lição. De seguida lê as informações referente à lição e completa o resto das atívidades. Tenta criar os projectos com os conhecimentos adquiridos na lição em vez de copiares o código diretamente da solução. No final ou caso tenhas dúvidas podes sempre olhar para o código fornecido na pasta /solutions para as lições em que são apresentados os projectos. Outra ideia seria criares um grupo de estudo com os teus amigos, de forma a aprenderem todos juntos. Se estiveres interessado em mais conteúdo de aprendizagem, recomendamos[Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **Para Estudantes**: para utilizares este cursão por conta própria, faz fork deste repositório e completa cada um dos exercícios, começando sempre pelo quiz pré lição. De seguida lê as informações referente à lição e completa o resto das atívidades. Tenta criar os projectos com os conhecimentos adquiridos na lição em vez de copiares o código diretamente da solução. No final ou caso tenhas dúvidas podes sempre olhar para o código fornecido na pasta /solutions para as lições em que são apresentados os projectos. Outra ideia seria criares um grupo de estudo com os teus amigos, de forma a aprenderem todos juntos. Se estiveres interessado em mais conteúdo de aprendizagem, recomendamos[Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
> **Дорогие учителя**, мы [добавили наши рекомендации](for-teachers.md) по работе с курсом. Мы будем рады получить ваши отзывы [на нашем форуме](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
> **Дорогие студенты**, для самостоятельного прохождения курса сделайте форк всего репозитория, выполните задания самостоятельно, начиная со вступительных тестов, а после прочтения лекции, выполните оставшуюся часть урока. Постарайтесь достигнуть понимания при выполнении заданий и избегайте копирования решения, несмотря на то, что решение доступно в папке `/solutions` для каждого мини-проекта. Отличной идеей также является организовать учебную группу со своими друзьями и пройти этот курс вместе. Для дальнейшего обучения мы рекомендуем портал [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa).
> **Дорогие студенты**, для самостоятельного прохождения курса сделайте форк всего репозитория, выполните задания самостоятельно, начиная со вступительных тестов, а после прочтения лекции, выполните оставшуюся часть урока. Постарайтесь достигнуть понимания при выполнении заданий и избегайте копирования решения, несмотря на то, что решение доступно в папке `/solutions` для каждого мини-проекта. Отличной идеей также является организовать учебную группу со своими друзьями и пройти этот курс вместе. Для дальнейшего обучения мы рекомендуем портал [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
> **Öğretmenler**: bu ders programını nasıl kullanacağınızla alakalı bazı [öneriler](../for-teachers.md) ekledik. [Tartışma forumlarımıza](https://github.com/microsoft/Data-Science-For-Beginners/discussions) bırakacağınız geribildirimlerinizi görmeyi çok isteriz!
> **Öğrenciler**: bu ders programını kendi başınıza kullanabilmek için tüm repoyu fork edin ve kendi başınıza ders öncesi kısa sınavlarından başlayarak alıştırmaları tamamlamaya çalışın. Sonra dersi okuyun ve geri kalan etkinlikleri tamamlayın. Çözüm kodunu kopyalamaktansa derslerde öğrendiklerinizi kullanarak projeler yaratmaya çalışın. Çözüm kodları her projeye dayalı dersin /solution klasöründe bulunmaktadır. Başka bir fikir de arkadaşlarınızla bir çalışma grubu kurup içeriği birlikte takip etmeniz olabilir. Daha ileri öğrenim için [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-40229-cxa)'ü tavsiye ediyoruz.
> **Öğrenciler**: bu ders programını kendi başınıza kullanabilmek için tüm repoyu fork edin ve kendi başınıza ders öncesi kısa sınavlarından başlayarak alıştırmaları tamamlamaya çalışın. Sonra dersi okuyun ve geri kalan etkinlikleri tamamlayın. Çözüm kodunu kopyalamaktansa derslerde öğrendiklerinizi kullanarak projeler yaratmaya çalışın. Çözüm kodları her projeye dayalı dersin /solution klasöründe bulunmaktadır. Başka bir fikir de arkadaşlarınızla bir çalışma grubu kurup içeriği birlikte takip etmeniz olabilir. Daha ileri öğrenim için [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)'ü tavsiye ediyoruz.
Pikachú
2 years ago
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