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Tuesday 25 August 2026 07:33:10 GMT
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echa_ameliaa
𝐄𝐜𝐡𝐚 𝐀𝐦𝐞𝐥𝐢𝐚 :
Bb 65 pake ukuran apa kak?
2026-10-07 14:49:36
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In Machine Learning, data doesn’t always come in CSV format. Two other very common types are JSON and SQL databases. 🔹 JSON (JavaScript Object Notation): • Data is stored in a key–value format, like a dictionary. • It’s widely used in web APIs and apps. • Example: A JSON file could contain student info with keys like name, age, grades. • Advantage: Easy to share between systems and supports nested/complex data. 🔹 SQL (Structured Query Language): • Data is stored in tables (rows and columns) inside a database. • You can use queries to fetch exactly the data you need. • Example: From a student database, you can query: “Get all students with GPA > 3.0”. • Advantage: Very efficient for managing large datasets. In ML Projects: 1️⃣ Sometimes you’ll pull raw data from APIs (JSON format). 2️⃣ Sometimes you’ll fetch structured data directly from databases (SQL). 3️⃣ You may even combine CSV, JSON, and SQL data into one dataset before analysis. 💡 Tip: As a data scientist/ML engineer, you should be comfortable handling data in all three forms — CSV, JSON, and SQL — because real-world data is never in just one format. #MachineLearning #JSON #SQL #DataScience #100DaysOfML #mljourney
In Machine Learning, data doesn’t always come in CSV format. Two other very common types are JSON and SQL databases. 🔹 JSON (JavaScript Object Notation): • Data is stored in a key–value format, like a dictionary. • It’s widely used in web APIs and apps. • Example: A JSON file could contain student info with keys like name, age, grades. • Advantage: Easy to share between systems and supports nested/complex data. 🔹 SQL (Structured Query Language): • Data is stored in tables (rows and columns) inside a database. • You can use queries to fetch exactly the data you need. • Example: From a student database, you can query: “Get all students with GPA > 3.0”. • Advantage: Very efficient for managing large datasets. In ML Projects: 1️⃣ Sometimes you’ll pull raw data from APIs (JSON format). 2️⃣ Sometimes you’ll fetch structured data directly from databases (SQL). 3️⃣ You may even combine CSV, JSON, and SQL data into one dataset before analysis. 💡 Tip: As a data scientist/ML engineer, you should be comfortable handling data in all three forms — CSV, JSON, and SQL — because real-world data is never in just one format. #MachineLearning #JSON #SQL #DataScience #100DaysOfML #mljourney

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