Unsupervised Learning Algorithms
Distance Learning · Further Education · Advanced Data Science and Analytics
This unit introduces unsupervised learning algorithms, including clustering, dimensionality reduction, and anomaly detection. Students will learn to apply these algorithms to real-world datasets to extract meaningful insights and make informed decisions.
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Das gehört dazu
- Types of Clustering Algorithms (e.g.
- K-Means
- Hierarchical Clustering)
- Dimensionality Reduction Techniques (e.g.
- PCA
- LDA)
- Anomaly Detection Methods (e.g.
- Isolation Forest
- Local Outlier Factor)
- Applications of Unsupervised Learning in Various Domains
- Evaluation Metrics for Unsupervised Learning Algorithms
- Challenges and Limitations of Unsupervised Learning
Stichworte
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Unsupervised Learning
- Machine Learning