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Machine Learning
Feature Engineering for Machine Learning
1. Introduction to Feature Engineering
2. Foundational Concepts
3. Exploratory Data Analysis for Features
4. Data Cleaning and Preparation
5. Feature Scaling and Normalization
6. Categorical Feature Engineering
7. Feature Creation and Generation
8. Temporal Feature Engineering
9. Text Feature Engineering
10. Geospatial Feature Engineering
11. Feature Selection Methods
12. Dimensionality Reduction
13. Advanced Feature Engineering
14. Evaluation and Validation
15. Implementation and Best Practices
16. Common Pitfalls and Solutions
12.
Dimensionality Reduction
12.1.
Linear Methods
12.1.1.
Principal Component Analysis
12.1.1.1.
Variance Explained
12.1.1.2.
Component Interpretation
12.1.1.3.
Scree Plots
12.1.1.4.
Biplot Analysis
12.1.2.
Linear Discriminant Analysis
12.1.3.
Independent Component Analysis
12.1.4.
Factor Analysis
12.1.5.
Canonical Correlation Analysis
12.2.
Non-Linear Methods
12.2.1.
t-SNE
12.2.2.
UMAP
12.2.3.
Isomap
12.2.4.
Locally Linear Embedding
12.2.5.
Multidimensional Scaling
12.3.
Neural Network Approaches
12.3.1.
Autoencoders
12.3.2.
Variational Autoencoders
12.3.3.
Deep Feature Learning
12.4.
Manifold Learning
12.4.1.
Manifold Assumption
12.4.2.
Local vs Global Methods
12.4.3.
Neighborhood Preservation
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11. Feature Selection Methods
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13. Advanced Feature Engineering