Dimensionality Reduction Basics
Introduction to PCA and t-SNE.
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Dimensionality Reduction Basics
Dimensionality reduction is the process of reducing the number of input variables (features) in a dataset while retaining essential information. It helps in simplifying data and improving computational efficiency.
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Why Dimensionality Reduction is Important
High-dimensional datasets can lead to:
- Overfitting: Models may capture noise instead of patterns.
- Increased Complexity: Computationally expensive to process.
- Visualization Challenges: Difficult to interpret data with many dimensions.
All lessons in this course
- Introduction to Clustering Algorithms
- K-Means Clustering
- K-Means Clustering Project
- Dimensionality Reduction Basics
- Dimensionality Reduction Application