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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
6.
Categorical Feature Engineering
6.1.
Nominal Feature Encoding
6.1.1.
One-Hot Encoding
6.1.2.
Dummy Variable Encoding
6.1.3.
Effect Encoding
6.1.4.
Binary Encoding
6.1.5.
BaseN Encoding
6.1.6.
Feature Hashing
6.2.
Ordinal Feature Encoding
6.2.1.
Label Encoding
6.2.2.
Ordinal Encoding
6.2.3.
Custom Ordinal Mapping
6.2.4.
Rank-Based Encoding
6.3.
High Cardinality Handling
6.3.1.
Frequency-Based Encoding
6.3.2.
Target-Based Encoding
6.3.2.1.
Mean Target Encoding
6.3.2.2.
Leave-One-Out Encoding
6.3.2.3.
Smoothed Target Encoding
6.3.2.4.
Bayesian Target Encoding
6.3.3.
Weight of Evidence Encoding
6.3.4.
Category Grouping
6.3.4.1.
Frequency Thresholding
6.3.4.2.
Similarity-Based Grouping
6.3.4.3.
Domain-Based Grouping
6.3.5.
Embedding Techniques
6.3.5.1.
Entity Embeddings
6.3.5.2.
Categorical Embeddings
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5. Feature Scaling and Normalization
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7. Feature Creation and Generation