Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering

Machine learning pipeline -- Fancy tricks with simple numbers -- Text data : flattening, filtering, and chunking -- 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