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Computer Science
Artificial Intelligence
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
13.
Advanced Feature Engineering
13.1.
Automated Feature Engineering
13.1.1.
Deep Feature Synthesis
13.1.2.
Genetic Programming
13.1.3.
AutoML Feature Generation
13.1.4.
Feature Learning
13.2.
Ensemble Feature Methods
13.2.1.
Feature Bagging
13.2.2.
Feature Boosting
13.2.3.
Stacked Features
13.3.
Cross-Validation in Feature Engineering
13.3.1.
Nested Cross-Validation
13.3.2.
Time Series Cross-Validation
13.3.3.
Stratified Cross-Validation
13.4.
Feature Engineering for Specific Algorithms
13.4.1.
Tree-Based Models
13.4.2.
Linear Models
13.4.3.
Neural Networks
13.4.4.
Distance-Based Models
13.4.5.
Ensemble Methods
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12. Dimensionality Reduction
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14. Evaluation and Validation