- Preface
- 1. The Machine Learning Pipeline
- 2. Fancy Tricks with Simple Numbers
- 3. Text Data: Flattening, Filtering, and Chunking
- 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
- 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens
- 6. Dimensionality Reduction: Squashing the Data Pancake with PCA
- 7. Nonlinear Featurization via K-Means Model Stacking
- 8. Automating the Featurizer: Image Feature Extraction and Deep Learning
- 9. Back to the Feature: Building an Academic Paper Recommender
- A. Linear Modeling and Linear Algebra Basics
- Index
-
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