With detailed notes, tables, and illustrations, this handy reference will allow you to navigate the fundamentals of machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional help during training and as a convenient source when you dive into your next machine learning project.
Ideal for programmers, data scientists, and AI engineers, this publication includes an overview of the machine learning procedure and walks you through classification together with organized data. You’ll also learn strategies for clustering, predicting a constant value (regression), and decreasing 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
- Dimensionality reduction
- Scikit-learn pipelines