Dimensionality Reduction
Machine Learning · Further Education · Machine Learning Applications
Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving the most important information. This unit will cover the different types of dimensionality reduction techniques, their applications, and how to choose the right technique for a given dataset.
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Das gehört dazu
- Introduction to dimensionality reduction
- Types of dimensionality reduction techniques
- Advantages and disadvantages of each dimensionality reduction technique
- Choosing the right dimensionality reduction technique for a given dataset
- Applying dimensionality reduction to a real-world dataset
Stichworte
- Dimensionality Reduction
- Principal Component Analysis
- Singular Value Decomposition
- Linear Discriminant Analysis
- Manifold Learning
- Feature Selection
- Data Analysis
- Machine Learning