Machine Learning Pocket Reference

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.

Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.

This pocket reference includes sections that cover:

Classification, using the Titanic dataset

Cleaning data and dealing with missing data

Exploratory data analysis

Common preprocessing steps using sample data

Selecting features useful to the model

Model selection

Metrics and classification evaluation

Regression examples using k-nearest neighbor, decision trees, boosting, and more

Metrics for regression evaluation

Clustering

Dimensionality reduction

Scikit-learn pipelines