Machine Learning algorithms fall into two primary categories:

1️⃣ Supervised Learning – This method relies on labeled datasets, where the input data comes with corresponding correct outputs. The model learns to predict outcomes based on past data. Common algorithms include:

Linear Regression – Predicts continuous values (e.g., housing prices).
Logistic Regression – Used for classification tasks (e.g., spam detection).
Decision Trees & Random Forests – Powerful models for both regression and classification.
Neural Networks – Deep learning models used for image and speech recognition.

2️⃣ Unsupervised Learning – Unlike supervised learning, this approach deals with unlabeled data. The model finds hidden patterns and structures in data without specific outputs. Common techniques include:

Clustering (e.g., K-Means, DBSCAN) – Groups similar data points together.
Dimensionality Reduction (e.g., PCA, t-SNE) – Reduces the number of features while preserving important information.
Anomaly Detection – Identifies unusual patterns in datasets (e.g., fraud detection in banking).

These two types of learning are fundamental to modern AI applications and automation.