Applied Machine Learning: Practical Models for Solving Real-World Business Problems

Business-focused machine learning begins with a clear view of the data and the decision it must support. The opening material establishes practical tools such as GitHub and Anaconda, introduces three use cases with dedicated datasets, and shows how visualization, descriptive statistics, correlation analysis, cleaning, and dummy coding shape dependable inputs.

The discussion then moves through a structured model-selection process. Readers compare regression, decision trees, random forests, gradient boosting, and clustering, while learning when each approach fits a business need. Validation metrics, interpretability, and iterative feature engineering provide a disciplined way to judge results, expose weak assumptions, and refine performance without treating the model as a black box.

The final stage connects analysis to operations through implementation, monitoring, prediction workflows, and impact measurement. Readers see how model quality must be maintained after launch and how outcomes can be linked to business value. By the end of this journey, readers can prepare data, choose and evaluate suitable models, and manage machine learning solutions from initial idea through long-term use.

© 2026 Packt Publishing (Libro electrónico): 9781808658327

Fecha de lanzamiento

Libro electrónico: 24 de julio de 2026