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