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Classroom Contents
Data Preprocessing and Feature Engineering for Machine Learning
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- 1 Intro
- 2 Uses of Machine Learning
- 3 Machine Learning Models
- 4 Data Format and Quality
- 5 Challenges of Feature Engineering
- 6 Open-source for Feature engineering
- 7 Why Open-source
- 8 Missing Data Imputation Techniques
- 9 Categorical Variables
- 10 Categorical Encoding Techniques
- 11 Encoding Techniques: Rare labels
- 12 Distributions
- 13 Mathematical transformations
- 14 Discretisation
- 15 Outliers
- 16 Feature Combination
- 17 Variable Magnitude
- 18 Feature scaling methods
- 19 Datetime Variables
- 20 Transactions and Time Series
- 21 Pipeline
