Learn from Data : Statistical Machine Learning with Python: Regression, Classification, and Neural Nets
Language: English
Published by Independently Published Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 52.04
Quantity: 2 available
Add to basketItem description from seller
Neuware - Regression, classification, and neural networks for practical predictive systemsMachine 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 mattersModern organizations rely on machine learning to: - predict outcomes and trends- classify and segment information- automate decision making- detect anomalies and fraud- personalize user experiences- uncover hidden patterns in dataUnderstanding the statistical foundations behind these systems is essential for building models that are reliable, interpretable, and useful.What you will learn- fundamentals of statistical learning- data preprocessing and feature engineering- regression modeling techniques- binary and multiclass classification- model evaluation and validation- bias, variance, and overfitting concepts- probability and statistical inference for ML- neural network fundamentals- optimization and gradient-based learning- building machine learning pipelines with PythonFrom raw data to predictive systemsThroughout the book, you will learn how to: - clean and prepare datasets effectively- select appropriate models for different problems- train and evaluate predictive systems- interpret model performance correctly- improve generalization and robustness- build maintainable machine learning workflowsEach chapter focuses on practical machine learning engineering principles rather than black-box shortcuts.Practical applications- business forecasting systems- fraud and anomaly detection- recommendation engines- customer behavior analysis- predictive analytics platforms- intelligent automation systemsThese examples reflect real-world machine learning engineering challenges.Who this book is for- aspiring machine learning engineers- data scientists- software developers entering AI- analysts learning predictive modeling- students studying machine learning- engineers building intelligent systemsIf 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.…
Seller Inventory # 9798180307477
- Title
- Learn from Data : Statistical Machine Learning with Python: Regression, Classification, and Neural Nets
- Author
- Richard Boozman
- Publisher
- Independently Published Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798180307477
- Item weight
- 308 grams
- Dimensions
- 229x152x16 mm
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 trends
- classify and segment information
- automate decision making
- detect anomalies and fraud
- personalize user experiences
- uncover 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 learn
- fundamentals of statistical learning
- data preprocessing and feature engineering
- regression modeling techniques
- binary and multiclass classification
- model evaluation and validation
- bias, variance, and overfitting concepts
- probability and statistical inference for ML
- neural network fundamentals
- optimization and gradient-based learning
- building machine learning pipelines with Python
From raw data to predictive systems
Throughout the book, you will learn how to:
- clean and prepare datasets effectively
- select appropriate models for different problems
- train and evaluate predictive systems
- interpret model performance correctly
- improve generalization and robustness
- build maintainable machine learning workflows
Each chapter focuses on practical machine learning engineering principles rather than black-box shortcuts.
Practical applications
- business forecasting systems
- fraud and anomaly detection
- recommendation engines
- customer behavior analysis
- predictive analytics platforms
- intelligent automation systems
These examples reflect real-world machine learning engineering challenges.
Who this book is for
- aspiring machine learning engineers
- data scientists
- software developers entering AI
- analysts learning predictive modeling
- students studying machine learning
- engineers 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.
"Synopsis" may belong to another edition of this title.
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