AI Unsupervised Learning Algorithms
Unsupervised learning algorithms are crucial in AI for uncovering patterns and structures within data without labeled examples. These algorithms operate on unlabeled data, seeking to identify inherent relationships and groupings. Clustering algorithms like K-means and hierarchical clustering partition data points into clusters based on similarity measures, while dimensionality reduction techniques such as PCA and t-SNE help visualize and simplify complex datasets. Anomaly detection methods like Z-score and Isolation Forest detect outliers, while association rule mining discovers interesting relationships within datasets. These unsupervised learning techniques empower AI systems to explore and understand data in an autonomous manner.
- Clustering
- K-Means clustering
- K-Means++ clustering
- K-Mode clustering
- Fuzzy C-Means (FCM) Clustering
- Gaussian mixture models (GMMs)
- Expectation-Maximization Algorithms
- Hierarchical clustering
- Affinity propagation
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- OPTICS (Ordering Points To Identify the Clustering Structure)
- Association Rule Mining
- Anomaly Detection:
- Dimensionality Reduction Technique:
Artificial Intelligence (AI) Algorithms
Artificial Intelligence (AI) is revolutionizing industries, transforming the way we interact with technology. With a growing interest in mastering AI, we’ve crafted a tutorial on AI algorithms, based on extensive research in the field. This tutorial covers core algorithms that serve as the backbone of artificially intelligent systems.
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