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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
Text Feature Engineering
Basic Text Features
Text Length
Word Count
Character Count
Sentence Count
Paragraph Count
Average Word Length
Average Sentence Length
Text Preprocessing
Tokenization
Lowercasing
Punctuation Removal
Stop Word Removal
Stemming
Lemmatization
Bag-of-Words Representations
Binary Bag-of-Words
Count Vectorization
Term Frequency
Document Frequency
TF-IDF Features
Term Frequency Calculation
Inverse Document Frequency
TF-IDF Weighting
Sublinear TF Scaling
N-gram Features
Unigrams
Bigrams
Trigrams
Character N-grams
Skip-grams
Advanced Text Features
Part-of-Speech Tags
Named Entity Recognition
Sentiment Scores
Readability Metrics
Language Detection
Text Similarity Measures
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8. Temporal Feature Engineering
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10. Geospatial Feature Engineering