Supervised Learning - tech9tel/ai GitHub Wiki

βœ… Supervised Learning

πŸ“˜ Definition: The model learns from a labeled dataset (input β†’ output).

🎯 Goal: Predict outputs for new, unseen inputs.

🍎 Analogy

Think of supervised learning like flashcards β€” the front has a picture of a fruit, and the back says the name. After enough practice, you can say the name just by looking at the front!

πŸ“˜ Official Definition

Supervised Learning is a type of machine learning where an algorithm is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to map inputs to the correct output during training so it can make accurate predictions on new, unseen data.

πŸ“˜ Simple Explanation

Supervised Learning is like learning with a teacher.

Imagine you're a kid learning to recognize fruits. Your parent shows you an apple and says, β€œThis is an apple.” Then they show you a banana and say, β€œThis is a banana.” Over time, you learn to recognize and name fruits correctly.

In supervised learning, a computer is trained in a similar way. It is given data with labels (like "apple" or "banana"), and it learns from those examples so it can predict the labels for new, unseen data.

βœ… Real-World Examples

  • Email Spam Detection: The system is trained with emails labeled β€œspam” or β€œnot spam.”
  • Medical Diagnosis: Using patient records labeled with conditions to predict future diagnoses.
  • Speech Recognition: The model learns from audio clips paired with written text.
  • Predicting house prices

πŸ” Popular Algorithms:

  • Linear Regression
  • Decision Trees
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)

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