Dimensionality Reduction
Artificial Intelligence · Further Education · Machine Learning Algorithms
Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving as much information as possible. This unit will cover the different types of dimensionality reduction techniques, their advantages and disadvantages, and how to apply them to real-world datasets.
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Das gehört dazu
- Introduction to dimensionality reduction
- Types of dimensionality reduction techniques
- Advantages and disadvantages of dimensionality reduction techniques
- Applications of dimensionality reduction
- Case studies of dimensionality reduction in practice
Stichworte
- Dimensionality Reduction
- Feature Reduction
- Principal Component Analysis
- Singular Value Decomposition
- Linear Discriminant Analysis
- Data Preprocessing
- Machine Learning
- Unsupervised Learning