- Data cleaning
- Missing data
- Outlier
- Others: duplicates, typos, special characters
- Feature engineering
- Strategy for missing data: imputation, mean, median, np.nan, unknown
- Outlier: visualize, demo of linear regression change with outlier, IQR
- Curse of dimensionality: count of columns aka features vs count of rows,
- Data transformation:
- Encoding
- Categorical, one hot encoding, machine readable, ordinal versus independent
- Scaling
- Skewed data
- Sampling
- Stratification
- Class imbalance
- Feature engineering
- Rank transformation
Key concepts
- One hot encoding: a categorical column of three potential values: married, single, divorced will become three separate columns of 1, 0
Core Data structures
- Pytorch tensors
- Tensorflow tensors
- Numpy ndarray
- Pandas dataframe and series
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