📌 Data Labelling Summary
Data labelling is the process of adding meaningful tags or labels to raw data so that machines can understand and learn from it. This often involves identifying objects in images, transcribing spoken words, or marking text with categories. Labels help computers recognise patterns and make decisions based on the data provided.
🙋🏻♂️ Explain Data Labelling Simply
Imagine sorting a box of mixed sweets by putting each sweet into a bag labelled with its flavour. Data labelling works the same way for information, helping computers know what each piece means. It is like giving each data point a clear name so a computer can learn to tell them apart.
📅 How Can it be used?
Data labelling can be used to tag thousands of photos with object names to train an image recognition system.
🗺️ Real World Examples
A company developing a self-driving car system collects thousands of street images and uses data labelling to mark where pedestrians, traffic lights, and road signs are located. This labelled data is used to train the car’s software to recognise and respond to these objects safely while driving.
A hospital wants to automate the detection of certain diseases in X-ray images. Medical experts label thousands of X-rays, indicating which ones show signs of illness. This labelled dataset is used to train an artificial intelligence model to spot diseases in new X-rays automatically.
✅ FAQ
What does data labelling mean?
Data labelling is the act of adding tags or labels to raw information so that computers can make sense of it. For example, you might draw boxes around cars in a photo or write out what someone says in a recording. These labels help computers spot patterns and learn to do things like recognise faces or understand spoken words.
Why is data labelling important for artificial intelligence?
Data labelling is important because it gives artificial intelligence systems the clear examples they need to learn. Without labels, computers would not know what to look for in the data. Proper labelling helps machines improve at tasks like sorting emails, translating languages or spotting objects in pictures.
Who usually does data labelling?
Data labelling is often done by people who carefully review and tag the information. Sometimes companies use large teams or even crowdsource the work to many people online. There are also tools that can help speed up the process, but humans are still needed to make sure the labels are correct and meaningful.
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