Table of Contents:
- The machine learning pipeline
- Fancy tricks with simple numbers
- Text data : flattening, filtering, and chunking
- The effects of feature scaling : from bag-of-words to Tf-Idf
- Categorical variables : counting eggs in the age of robotic chickens
- Dimensionality reduction : squashing the data pancake with PCA
- Nonlinear featurization via K-means model stacking
- Automating the featurizer : image feature extraction and deep learning
- Back to the feature : building an academic paper recommender
- Linear modeling and linear algebra basics.
