Learn from Data: Statistical Machine Learning with Python: Regression, Classification, and Neural Nets

By RICHARD BOOZMAN

Regression, classification, and neural networks for practical predictive systems

Machine learning is not magic-it is applied mathematics, statistics, and engineering working together to extract patterns from data.

Behind every recommendation engine, fraud detector, forecasting model, and intelligent application lies a foundation of statistical reasoning and predictive modeling.

"Learn from Data" is a practical, engineering-focused guide to statistical machine learning using Python and modern data science workflows.

This book teaches developers and analysts how to build, evaluate, and improve machine learning systems through clear explanations, hands-on examples, and real-world problem solving.

Why statistical machine learning matters

Modern organizations rely on machine learning to:

predict outcomes and trendsclassify and segment informationautomate decision makingdetect anomalies and fraudpersonalize user experiencesuncover hidden patterns in data

Understanding the statistical foundations behind these systems is essential for building models that are reliable, interpretable, and useful.

What you will learnfundamentals of statistical learningdata preprocessing and feature engineeringregression modeling techniquesbinary and multiclass classificationmodel evaluation and validationbias, variance, and overfitting conceptsprobability and statistical inference for MLneural network fundamentalsoptimization and gradient-based learningbuilding machine learning pipelines with Python
From raw data to predictive systems

Throughout the book, you will learn how to:

clean and prepare datasets effectivelyselect appropriate models for different problemstrain and evaluate predictive systemsinterpret model performance correctlyimprove generalization and robustnessbuild maintainable machine learning workflows

Each chapter focuses on practical machine learning engineering principles rather than black-box shortcuts.

Practical applicationsbusiness forecasting systemsfraud and anomaly detectionrecommendation enginescustomer behavior analysispredictive analytics platformsintelligent automation systems

These examples reflect real-world machine learning engineering challenges.

Who this book is foraspiring machine learning engineersdata scientistssoftware developers entering AIanalysts learning predictive modelingstudents studying machine learningengineers building intelligent systems

If you want to understand how machine learning works mathematically and practically, this book provides the roadmap.

Model carefully.
Learn from data.
Build predictive systems with confidence.

• Format: Paperback
• Language: English
• ISBN: B0H4B38FGF
• ISBN13: 9798180307477
• Release Date: June 2026
• Publisher: Independently Published
• Length: 250 Pages
• Weight: 0.68 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"