Machine learning encompasses various techniques aimed at enabling systems to learn from data and improve over time. Below are key categories and methods:
📊 Supervised Learning
- Definition: Uses labeled data to train models.
- Common Algorithms: Linear Regression, Decision Trees, Support Vector Machines (SVM), Neural Networks.
- Applications: Predictive analytics, classification tasks (e.g., spam detection).
🧠 Unsupervised Learning
- Definition: Finds patterns in unlabeled data.
- Common Algorithms: K-Means Clustering, Principal Component Analysis (PCA), Autoencoders.
- Applications: Customer segmentation, anomaly detection.
🔄 Reinforcement Learning
- Definition: Trains models through reward-based feedback.
- Common Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradients.
- Applications: Game playing (e.g., AlphaGo), robotics.
🌐 Related Resources
For visual comparisons of techniques, check out our interactive guide. 📈