Machine learning algorithms and examples
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn automatically from data — without being explicitly programmed.
How It Works (Basic Steps)
Collect Data → e.g., customer data, images, text, etc.
Prepare Data → clean, normalize, and split into train/test sets.
Choose Model → decide which ML algorithm fits (like regression, decision tree, etc.).
Train Model → feed data to the algorithm so it learns patterns.
Test Model → check how accurately it predicts on unseen data.
Deploy Model → use it in an app or API for real-world predictions.
Supervised Learning :Learn from labeled data (input → output known)
Unsupervised Learning: Find hidden patterns (no labels)
Reinforcement Learning: Learn by trial and error
Common Algorithms
🔹 Supervised Learning
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Support Vector Machine (SVM)
K-Nearest Neighbors (KNN)
🔹 Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
🔹 Reinforcement Learning
Q-Learning
Deep Q Networks (DQN)