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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
5.
Feature Scaling and Normalization
5.1.
Scaling Fundamentals
5.1.1.
Need for Scaling
5.1.1.1.
Algorithm Sensitivity
5.1.1.2.
Distance-Based Methods
5.1.1.3.
Gradient Descent Optimization
5.1.1.4.
Feature Magnitude Differences
5.1.2.
Scale-Invariant vs Scale-Dependent Algorithms
5.2.
Standardization Techniques
5.2.1.
Z-Score Standardization
5.2.2.
Robust Standardization
5.2.3.
Unit Vector Scaling
5.2.4.
Max-Abs Scaling
5.3.
Normalization Methods
5.3.1.
Min-Max Normalization
5.3.2.
Decimal Scaling
5.3.3.
Vector Normalization
5.4.
Distribution Transformation
5.4.1.
Log Transformation
5.4.2.
Square Root Transformation
5.4.3.
Reciprocal Transformation
5.4.4.
Box-Cox Transformation
5.4.5.
Yeo-Johnson Transformation
5.4.6.
Quantile Transformation
5.4.7.
Power Transformations
5.5.
Discretization and Binning
5.5.1.
Equal Width Binning
5.5.2.
Equal Frequency Binning
5.5.3.
K-Means Binning
5.5.4.
Decision Tree Binning
5.5.5.
Custom Binning Strategies
5.5.6.
Optimal Binning Methods
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4. Data Cleaning and Preparation
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6. Categorical Feature Engineering