Supervised Learning #

Trained using labelled data.
Each example in the training set includes the correct output.
The algorithm learns to generalise and make predictions on unseen data.
Generally more accurate than unsupervised methods.
Requires human intervention for labelling and setup.
Widely used due to its accuracy and efficiency.
Produces highly accurate results when trained on good-quality labelled data.

mindmap
  root(Supervised Learning)
      Regression
        Linear Regression
        Multiple Linear Regression
        Polynomial Regression
        Logistic Regression
      Classification
        Binary Classification
        Multi Class Classification
        Multi Label Classification

Classification #

Output is discrete (e.g. Yes/No, Spam/Not Spam).
Used for categorising data into predefined classes.
Support Vector Machine (SVM) is a common classifier (a linear classifier with margin-based separation).

Common Types of Classification #

Binary Classification – Two possible outcomes (e.g. Spam / Not Spam).
Multi-Class Classification – More than two outcomes (e.g. Classifying animals as Cat, Dog, Bird).
Multi-Label Classification – One instance can belong to multiple categories (e.g. A movie tagged as Action and Comedy).

Regression #

Output is a continuous value (e.g. price, temperature).
Predicts numeric outcomes rather than categories.

Common Types of Regression #

Linear Regression – Straight-line relationship between input and output.
Multiple Linear Regression – Multiple features predict one output.
Polynomial Regression – Curved relationships.
Logistic Regression – Used for classification (predicting probabilities).

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