- 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
About the Authors
Alice Zheng is a technical leader in applied machine learning, spanning algorithm and platform development. Currently, she is a research science manager in Amazon Advertising. Previously, she worked on toolkit development and user education at GraphLab/Dato/Turi, and was a machine learning researcher at Microsoft Research. She holds a PhD in electrical engineering and computer science, and BA degrees in computer science and mathematics, all from UC Berkeley.
Amanda Casari is a leader and engineer who explores the next horizons of technology and how to best demonstrate the impacts these will bring. She is currently a senior product manager and data scientist in Concur Labs and cofounder of the Concur Labs AI Research team at SAP Concur. She has worked in a breadth of cross-functional roles and engineering disciplines for the last 16 years, including data science, machine learning, complex systems, and robotics. Amanda holds a BS in control systems engineering from the United States Naval Academy and an MS in electrical engineering from the University of Vermont.
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