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@yudiiithm: No complicadas #CapCut #parati #pioneertemplate #capcutpioneer #embajadorcc

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Thursday 08 October 2026 01:41:05 GMT
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In the real world, data is not always stored in files like CSV or databases. Many times, we get data directly from the internet using APIs (Application Programming Interfaces). 🔹 What is an API? • An API is like a bridge that allows two systems to talk to each other. • It provides data in formats like JSON or XML. 🔹 Why APIs are important in ML? • You can collect real-time data (e.g., weather updates, stock prices, traffic info). • No need to manually download files — data comes automatically. • Useful for building live ML models that adapt to changing data. 🔹 Examples: • Using the Twitter API to fetch tweets for sentiment analysis. • Using the OpenWeather API to get temperature and humidity for a weather prediction model. • Using the YouTube API to collect comments for text analysis. 🔹 In ML Projects: 1️⃣ You send a request to the API. 2️⃣ The API sends back data (usually JSON). 3️⃣ You clean and use that data to train/test your model. 💡 Tip: APIs make your ML projects more dynamic, scalable, and realistic because you’re not limited to static datasets. #MachineLearning #APIs #DataScience #100DaysOfML #mljourney
In the real world, data is not always stored in files like CSV or databases. Many times, we get data directly from the internet using APIs (Application Programming Interfaces). 🔹 What is an API? • An API is like a bridge that allows two systems to talk to each other. • It provides data in formats like JSON or XML. 🔹 Why APIs are important in ML? • You can collect real-time data (e.g., weather updates, stock prices, traffic info). • No need to manually download files — data comes automatically. • Useful for building live ML models that adapt to changing data. 🔹 Examples: • Using the Twitter API to fetch tweets for sentiment analysis. • Using the OpenWeather API to get temperature and humidity for a weather prediction model. • Using the YouTube API to collect comments for text analysis. 🔹 In ML Projects: 1️⃣ You send a request to the API. 2️⃣ The API sends back data (usually JSON). 3️⃣ You clean and use that data to train/test your model. 💡 Tip: APIs make your ML projects more dynamic, scalable, and realistic because you’re not limited to static datasets. #MachineLearning #APIs #DataScience #100DaysOfML #mljourney

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