{"id":170,"date":"2026-03-03T09:43:40","date_gmt":"2026-03-03T04:43:40","guid":{"rendered":"https:\/\/gigz.pk\/python\/?post_type=lesson&p=170"},"modified":"2026-03-17T09:13:41","modified_gmt":"2026-03-17T04:13:41","slug":"supervised-vs-unsupervised-learning","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/python\/lesson\/supervised-vs-unsupervised-learning\/","title":{"rendered":"Supervised vs Unsupervised Learning"},"content":{"rendered":"\n

Supervised and Unsupervised Learning are two main types of Machine Learning.<\/p>\n\n\n\n

The key difference between them is whether the data has labels (correct answers) or not.<\/p>\n\n\n\n

What is Supervised Learning?<\/h2>\n\n\n\n

Supervised Learning uses labeled data.<\/p>\n\n\n\n

This means the dataset contains:<\/p>\n\n\n\n

Input data + Correct output<\/p>\n\n\n\n

The model learns the relationship between input and output to make predictions.<\/p>\n\n\n\n

Example<\/h3>\n\n\n\n

If we want to predict house prices:<\/p>\n\n\n\n

Input \u2192 Size, Location, Bedrooms
Output \u2192 Price<\/p>\n\n\n\n

The model learns from past data where the price is already known.<\/p>\n\n\n\n

Common Supervised Learning Algorithms<\/h3>\n\n\n\n

Linear Regression
Logistic Regression
Decision Trees
Random Forest
Support Vector Machines
Neural Networks<\/p>\n\n\n\n

Types of Supervised Learning<\/h3>\n\n\n\n
    \n
  1. Regression \u2192 Predicts continuous values
    Example: House price prediction<\/li>\n\n\n\n
  2. Classification \u2192 Predicts categories
    Example: Spam or Not Spam<\/li>\n<\/ol>\n\n\n\n

    Real-Life Examples<\/h3>\n\n\n\n

    Email spam detection
    Student result prediction
    Fraud detection
    Medical diagnosis<\/p>\n\n\n\n

    What is Unsupervised Learning?<\/h2>\n\n\n\n

    Unsupervised Learning uses unlabeled data.<\/p>\n\n\n\n

    This means the dataset contains:<\/p>\n\n\n\n

    Input data only (no correct output)<\/p>\n\n\n\n

    The model tries to find patterns, groups, or structure in the data.<\/p>\n\n\n\n

    Example<\/h3>\n\n\n\n

    Customer data without labels.<\/p>\n\n\n\n

    The model groups customers based on buying behavior.<\/p>\n\n\n\n

    Common Unsupervised Learning Algorithms<\/h3>\n\n\n\n

    K-Means Clustering
    Hierarchical Clustering
    DBSCAN
    Principal Component Analysis (PCA)<\/p>\n\n\n\n

    Types of Unsupervised Learning<\/h3>\n\n\n\n
      \n
    1. Clustering \u2192 Group similar data
      Example: Customer segmentation<\/li>\n\n\n\n
    2. Association \u2192 Find relationships
      Example: Market basket analysis<\/li>\n<\/ol>\n\n\n\n

      Real-Life Examples<\/h3>\n\n\n\n

      Customer segmentation
      Recommendation systems
      Anomaly detection
      Market analysis<\/p>\n\n\n\n

      Key Differences<\/h2>\n\n\n\n

      Supervised Learning:<\/p>\n\n\n\n