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LinkedIn Learning

Applied Machine Learning: Feature Engineering

via LinkedIn Learning

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Overview

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Extract the maximum value from your data using feature engineering. Learn how to clean, normalize, and create features to improve the performance of your machine learning models.

Syllabus

Introduction
  • Applied ML: Feature engineering
  • What you should know
1. Basic Techniques
  • Imputation
  • Filling in missing values
  • Binning
  • Log transform
  • Scaling
  • Challenge: Basic techniques
  • Solution: Basic techniques
2. Categorical Encoding
  • One hot encoding
  • Hashing encoder
  • Mean target encoding
  • Challenge: Categorical
  • Solution: Categorical
3. Feature Extraction
  • PCA
  • Feature aggregation
  • TFIDF
  • Text embeddings
  • Challenge: Feature extraction
  • Solution: Feature extraction
4. Temporal Features
  • Extracting date components
  • Seasonality and trend decomposition
  • Challenge: Temporal features
  • Solution: Temporal features
5. Feature Evaluation
  • Importance and weights
  • Recursive feature elimination
  • Adding a random column
  • Challenge: Feature selection
  • Solution: Feature selection
Conclusion
  • Next steps

Taught by

Derek Jedamski

Reviews

4.9 rating at LinkedIn Learning based on 14 ratings

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