Machine Learning Pocket Reference
Working with Structured Data in Python

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.

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

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.

Matt Harrison is a Python user, presenter, author, and user group organizer. He helps run the Utah Python user group. He authored the best-selling Treading on Python Vol 1 & 2 books. He runs MetaSnake, a Python training and consultancy shop.