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- Data
Feature Engineering
6 Courses
17 Hours
Skill IQ
Feature engineering is the process of using domain knowledge and insight into data to define features that enable machine learning algorithms to work successfully. Feature engineering is a fundamental part of the data preparation workflow for machine learning solutions.
Content in this path
Beginner
Learn how feature engineering fits into the machine learning workflow, and build your first features from numerical data.
Intermediate
Transform nominal data, such as names or categories, into features appropriate for machine learning, and apply techniques for simplifying large data sets.
Advanced
Extract features from text documents and images.
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What You'll Learn
- Qualities of effective features and how to assess them
- Numeric techniques (quantization binning, binarization, transforms, scaling, normalization)
- Text techniques (bag-of-x, filtering, n-grams, phrase detection)
- Categorical data techniques (one-hot encoding, hashing, bin counting, etc)
- Dimensionality reduction (PCA)
- Nonlinear featurization (K-means clustering
- model stacking)
- Image processing techniques (feature extraction)
Prerequisites
- Data Literacy
- Data Analytics Literacy
- Statistics
- Machine Learning Literacy
Related topics
- Data sourcing
- Data validation
- Data transformation and manipulation
- Data engineering
- Sampling
- Statistics
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