List of Listings

Chapter 1. What is machine learning?

Listing 1.1. Initial structure of an ML workflow program

Listing 1.2. Our ML workflow program with model evaluation

Chapter 2. Real-world data

Listing 2.1. Convert categorical features to numerical binary features

Listing 2.2. Simple feature extraction on Titanic cabins

Listing 2.3. Feature normalization

Chapter 3. Modeling and prediction

Listing 3.1. Building a logistic regression classifier with scikit-learn

Chapter 4. Model evaluation and optimization

Listing 4.1. Cross-validation with the holdout method

Listing 4.2. Cross-validation with k-fold cross-validation

Listing 4.3. The ROC curve

Listing 4.4. The area under the ROC curve

Listing 4.5. The root-mean-square error

Listing 4.6. The R-squared calculation

Listing 4.7. Grid search with kernel SVM

Chapter 6. Example: NYC taxi data

Listing 6.1. Logistic regression tip-prediction model

Listing 6.2. Random forest tip-prediction model

Listing 6.3. Converting categorical columns to numerical features

Listing 6.4. Date-time features

Chapter 7. Advanced feature engineering

Listing 7.1. Latent semantic analysis using scikit-learn

Listing 7.2. Latent Dirichlet analysis in Python using Gensim

Listing 7.3. Histogram of oriented gradients in Python with scikit-image

Listing 7.4. Converting SF crime event data to classical time series

Listing 7.5. Windowed statistics and differences

Listing 7.6. Periodogram features

Chapter 8. Advanced NLP example: movie review sentiment