What is the difference between supervised and unsupervised learning?

Supervised Learning:

1. Data Type:

   • Supervised learning requires labeled data, where each example in the dataset is paired with a corresponding label or target output.

2. Objective:

   • The goal of supervised learning is to learn a mapping from input data to output labels or predictions.

3. Feedback Mechanism:

   • Algorithms receive feedback on their predictions by comparing them to the actual labels in the training data. This feedback loop helps the algorithm adjust its parameters to minimize prediction errors.

4. Examples:

   • Common tasks in supervised learning include classification (e.g., email spam detection, image recognition) and regression (e.g., predicting house prices, stock market forecasting).

Unsupervised Learning:

1. Data Type:

   • Unsupervised learning operates on unlabeled data, where the algorithm is provided with input data without corresponding output labels.

2. Objective:

   • The objective of unsupervised learning is to discover patterns, structures, or relationships within the data without explicit guidance.

3. Feedback Mechanism:

   • Since there are no labeled examples, unsupervised learning algorithms do not receive explicit feedback on their predictions. Instead, they autonomously uncover hidden patterns in the data.

4. Examples:

   • Clustering (e.g., grouping similar customers based on purchasing behavior), dimensionality reduction (e.g., reducing the number of features in a dataset while preserving important information), and anomaly detection (e.g., identifying unusual patterns in data) are common tasks in unsupervised learning.