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
