This page provides an overview of different machine learning techniques and their comparisons. For more detailed information, you can check out our Machine Learning Basics section.
Types of Machine Learning
Supervised Learning
- Uses labeled training data.
- Algorithms learn from past data to make predictions.
- Example: Linear Regression, Decision Trees, Neural Networks.
Unsupervised Learning
- Uses unlabeled data.
- Algorithms find patterns and relationships in the data.
- Example: Clustering, Association Rules, Dimensionality Reduction.
Reinforcement Learning
- Involves an agent that learns to make decisions by performing actions in an environment to achieve a goal.
- Example: Q-Learning, Policy Gradients.
Comparison Table
| Technique | Inputs | Outputs | Use Cases |
|---|---|---|---|
| Supervised Learning | Labeled data | Predictions | Image recognition, Sentiment analysis |
| Unsupervised Learning | Unlabeled data | Patterns, clusters | Customer segmentation, Anomaly detection |
| Reinforcement Learning | State, action, reward | Decision policies | Game playing, Robotics |
Visual Representation
Here's a visual representation of the different types of machine learning:
For further reading, you can visit our Machine Learning Resources page.