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Learn from Data: Statistical Machine Learning with Python: Regression, Classification, and Neural Nets - Softcover
Synopsis
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 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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Learn from Data (Paperback)
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Paperback. Condition: new. Paperback. 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 trendsclassify and segment informationautomate decision makingdetect anomalies and fraudpersonalize user experiencesuncover hidden patterns in dataUnderstanding 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 PythonFrom raw data to predictive systemsThroughout 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 workflowsEach 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 systemsThese 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 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798180307477
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Learn from Data: Statistical Machine Learning with Python: Regression, Classification, and Neural Nets
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Learn from Data
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Learn from Data (Paperback)
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Paperback. Condition: new. Paperback. 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 trendsclassify and segment informationautomate decision makingdetect anomalies and fraudpersonalize user experiencesuncover hidden patterns in dataUnderstanding 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 PythonFrom raw data to predictive systemsThroughout 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 workflowsEach 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 systemsThese 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 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798180307477
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Learn from Data : Statistical Machine Learning with Python: Regression, Classification, and Neural Nets
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Taschenbuch. Condition: Neu. 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
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