{"id":59,"date":"2026-04-03T11:02:10","date_gmt":"2026-04-03T11:02:10","guid":{"rendered":"https:\/\/gigz.pk\/ml\/?post_type=lesson&p=59"},"modified":"2026-04-07T06:09:00","modified_gmt":"2026-04-07T06:09:00","slug":"intro-to-supervised-learning","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/ml\/lesson\/intro-to-supervised-learning\/","title":{"rendered":"\u00a0Intro to Supervised Learning"},"content":{"rendered":"\n

Supervised Learning is a type of Machine Learning where the model is trained using labeled data. In labeled data, each input comes with a corresponding output or target. The goal of supervised learning is for the model to learn the relationship between inputs and outputs so it can make accurate predictions on new, unseen data.<\/p>\n\n\n\n

How Supervised Learning Works<\/h2>\n\n\n\n
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  1. Collect Data:<\/strong> Gather a dataset that contains inputs (features) and their corresponding outputs (labels).<\/li>\n\n\n\n
  2. Train the Model:<\/strong> The model learns patterns and relationships from the training data.<\/li>\n\n\n\n
  3. Test the Model:<\/strong> Evaluate the model on new data that it hasn\u2019t seen before to check its performance.<\/li>\n\n\n\n
  4. Make Predictions:<\/strong> Once trained, the model can predict outputs for new inputs.<\/li>\n<\/ol>\n\n\n\n

    Types of Supervised Learning<\/h2>\n\n\n\n

    Regression<\/h3>\n\n\n\n

    Regression is used when the output is a continuous value. Examples include predicting house prices, temperature, or sales numbers.<\/p>\n\n\n\n

    Classification<\/h3>\n\n\n\n

    Classification is used when the output is a category or class. Examples include email spam detection, disease diagnosis (yes\/no), or customer segmentation.<\/p>\n\n\n\n

    Key Concepts in Supervised Learning<\/h2>\n\n\n\n