Machine learning algorithms are the backbone of predictive models, enabling systems to learn from data. This page explores three primary categories: supervised, unsupervised, and reinforcement learning.
Supervised Learning
Supervised learning uses labeled data to train models that predict outcomes. The model minimizes error by adjusting its parameters based on known outputs.
Examples include:
- Linear Regression: Predicts continuous values (e.g., house prices).
- Logistic Regression: Classifies binary outcomes (e.g., spam vs. not spam).
- Decision Trees: Splits data into branches for interpretable predictions.
- Neural Networks: Models complex patterns for tasks like image recognition.
Below is a Python example using scikit-learn for linear regression:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
fit(X_train, y_train)
predictions = model.predict(X_test)
Applications: Fraud detection, medical diagnosis.
Unsupervised Learning
Unsupervised learning identifies patterns in unlabeled data without predefined outputs.
Examples include:
- K-Means Clustering: Groups data into k clusters.
- PCA: Reduces dimensionality while retaining variance.
- Autoencoders: Neural networks for feature extraction.
Applications: Customer segmentation, anomaly detection.
Reinforcement Learning
Reinforcement learning trains agents to maximize rewards through trial and error in an environment.
Examples include:
- Q-Learning: Learns action values in discrete states.
- Deep Q-Networks: Extends Q-learning with neural networks.
- Policy Gradients: Optimizes decision policies directly.
Applications: Robotics, game AI (e.g., AlphaGo).