Principal Component Analysis
Data Science · Further Education · Machine Learning Algorithms
This unit introduces Principal Component Analysis (PCA), a technique for reducing the dimensionality of data by identifying the most important features. By the end of this unit, you will be able to apply PCA to real-world datasets and interpret the results.
Zum Üben gibt es 48 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Mathematical Foundations of PCA
- PCA Algorithm
- Interpreting PCA Results
- Applications of PCA
- Advantages and Limitations of PCA
Stichworte
- Principal Components
- Eigenvectors
- Eigenvalues
- Dimensionality Reduction
- Data Transformation
- Variance
- Covariance
- Linear Algebra