Wei shouke (31 resultados)

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

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Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condición: new. Paperback. Data is abundant, but understanding is not. Between raw data and meaningful decisions lies a crucial process: the ability to build, evaluate, and refine models that capture structure in the world. This book, Practical Data Modeling and Machine Learning with Python, focuses on that process.It is the second volume in the *Practical Data Science with Python* series. The first book introduced data exploration and visualization-how to observe patterns, clean data, and ask the right questions. This volume moves one step further: from understanding data to **modeling it**, and from intuition to quantitative reasoning.Purpose of This Book The central goal of this book is not simply to present algorithms, but to develop a coherent approach to **data modeling**.In practice, modeling is not a single step. It is a system: defining a problem clearlypreparing data carefullyselecting appropriate modelsevaluating performance rigorouslyrefining and improving resultsThis book follows that system. It integrates statistical modeling and modern machine learning into a unified workflow, emphasizing both principles and practical implementation.What This Book CoversThis book is organized into six parts, each corresponding to a key stage in the data modeling and machine learning workflow. Part I - Foundations of Data Modeling introduces the fundamental concepts of data modeling and analytical thinking. It covers the practical setup of a Python environment and the essential steps of data preparation and feature engineering, establishing a solid foundation for all subsequent work.Part II - Statistical Modeling Foundations provides the necessary statistical background for modeling. Topics such as probability distributions, estimation, and hypothesis testing are presented with a focus on interpretation and practical relevance.Part III - Statistical Modeling Techniques develops core modeling approaches, including linear regression, regularization, and generalized linear models. These methods form the bridge between classical statistics and modern machine learning.Part IV - Foundations of Machine Learning introduces the principles that govern machine learning systems, including training and validation strategies, the bias-variance tradeoff, and the role of cross-validation and preprocessing pipelines in building reliable models.Part V - Core Machine Learning Models presents practical machine learning methods, including classification models, regression techniques, and ensemble approaches. Emphasis is placed on understanding model behavior and comparing different methods in realistic settings.Part VI - Model Evaluation and Optimization focuses on assessing and improving models. It covers performance metrics, validation strategies, hyperparameter tuning, and model interpretation techniques, providing a complete framework for building robust and trustworthy models. Together, these parts form a coherent progression from data preparation to model evaluation and optimization, reflecting the full lifecycle of data-driven modeling. Rather than focusing only on algorithms, this book emphasizes how to think about modeling problems, avoid common pitfalls, and develop reliable solutions in practice. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. This first volume, Foundations and Core Wavelet Theory: Concepts, Mathematics, and PythonFoundations, establishes the theoretical and computational groundwork for the volumes thatfollow. It introduces the fundamental principles of wavelet analysis, including core mathematicalconstructs and their relationship to classical signal-processing methods. Building upon thisfoundation, the volume presents essential discrete wavelet techniques-implemented usingthe PyWavelets library-while maintaining a consistent emphasis on clarity, structure, andreproducibility. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Building upon the theoretical foundations established in Volume I and the methodological developments explored in Volume II, this volume, Financial, Security, and Digital Systems: Finance, Data Security, Signal Processing, and Anomaly Detection turns toward application domains in which signals arise primarily from digital infrastructure and economic systems. Financial markets, communication networks, and digital media streams produce time series that are noisy, nonstationary, and often shaped by complex human and technological interactions. Wavelet-based analysis offers a powerful framework for examining these signals because it enables localized multiscale representations that capture transient events, regime shifts, and hidden structural patterns. The chapters in this volume examine several domains in which multiscale analysis provides meaningful analytical insight. These include wavelet-based approaches to data security and steganography, where transform-domain representations enable robust information embedding and detection; financial time-series analysis, where wavelet decompositions help reveal volatility dynamics and cross-scale interactions in market behavior; digital audio signal analysis, where wavelets support efficient representation, denoising, and feature extraction; and multiscale anomaly detection, where wavelet-based features assist in identifying abnormal events within complex systems. A recurring theme throughout this volume is the relationship between multiscale representation and interpretability. Signals generated by financial markets, communication systems, and digital media frequently exhibit behaviors that manifest differently across time scales. Wavelet transforms provide a means of separating these scales, but meaningful interpretation requires careful consideration of system context, domain knowledge, and the mechanisms that generate the observed data. For this reason, the emphasis in this volume extends beyond algorithmic implementation to include analytical reasoning and system-aware interpretation. Consistent with the philosophy of the series, all examples are implemented using reproducible Python workflows. The objective is not to introduce novel algorithms, but to demonstrate how wavelet-based analysis can be integrated into real analytical pipelines used for monitoring, detection, and interpretation. Readers are encouraged to adapt the provided implementations to their own data sources and operational systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. Water quality degradation and water scarcity are two serious problems, and water resources management related to those problems usually involves conflicts. In the absence of market and exclusive property rights, those conflicts are unavoidable. Game theory is an appropriate approach to simulate and resolve such conflicts. The overall objective of this book is to develop a practical methodology and mechanism to promote the maximization of public welfare from a socio-economic and environmental perspective using game theory. There are three main goals: 1) to use game theory to illustrate and analyze phenomena of water resources management, 2) to set up non-cooperative and cooperative game theoretic simulation models of water conflicts in a river basin, and 3) to apply the game theoretic simulation approach into water conflicts involved in the Hanjiang River Basin (HRB) in China. The game theoretic simulation approach and results benefit not only different groups of water stakeholders, but also decision makers to make policies on water quantity allocation, water quality protection, water pricing as well as ecological compensation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. This first volume, Foundations and Core Wavelet Theory: Concepts, Mathematics, and PythonFoundations, establishes the theoretical and computational groundwork for the volumes thatfollow. It introduces the fundamental principles of wavelet analysis, including core mathematicalconstructs and their relationship to classical signal-processing methods. Building upon thisfoundation, the volume presents essential discrete wavelet techniques-implemented usingthe PyWavelets library-while maintaining a consistent emphasis on clarity, structure, andreproducibility. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Data is abundant, but understanding is not. Between raw data and meaningful decisions lies a crucial process: the ability to build, evaluate, and refine models that capture structure in the world. This book, Practical Data Modeling and Machine Learning with Python, focuses on that process.It is the second volume in the *Practical Data Science with Python* series. The first book introduced data exploration and visualization-how to observe patterns, clean data, and ask the right questions. This volume moves one step further: from understanding data to **modeling it**, and from intuition to quantitative reasoning.Purpose of This Book The central goal of this book is not simply to present algorithms, but to develop a coherent approach to **data modeling**.In practice, modeling is not a single step. It is a system: defining a problem clearlypreparing data carefullyselecting appropriate modelsevaluating performance rigorouslyrefining and improving resultsThis book follows that system. It integrates statistical modeling and modern machine learning into a unified workflow, emphasizing both principles and practical implementation.What This Book CoversThis book is organized into six parts, each corresponding to a key stage in the data modeling and machine learning workflow. Part I - Foundations of Data Modeling introduces the fundamental concepts of data modeling and analytical thinking. It covers the practical setup of a Python environment and the essential steps of data preparation and feature engineering, establishing a solid foundation for all subsequent work.Part II - Statistical Modeling Foundations provides the necessary statistical background for modeling. Topics such as probability distributions, estimation, and hypothesis testing are presented with a focus on interpretation and practical relevance.Part III - Statistical Modeling Techniques develops core modeling approaches, including linear regression, regularization, and generalized linear models. These methods form the bridge between classical statistics and modern machine learning.Part IV - Foundations of Machine Learning introduces the principles that govern machine learning systems, including training and validation strategies, the bias-variance tradeoff, and the role of cross-validation and preprocessing pipelines in building reliable models.Part V - Core Machine Learning Models presents practical machine learning methods, including classification models, regression techniques, and ensemble approaches. Emphasis is placed on understanding model behavior and comparing different methods in realistic settings.Part VI - Model Evaluation and Optimization focuses on assessing and improving models. It covers performance metrics, validation strategies, hyperparameter tuning, and model interpretation techniques, providing a complete framework for building robust and trustworthy models. Together, these parts form a coherent progression from data preparation to model evaluation and optimization, reflecting the full lifecycle of data-driven modeling. Rather than focusing only on algorithms, this book emphasizes how to think about modeling problems, avoid common pitfalls, and develop reliable solutions in practice. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Real-world modeling problems rarely conform to the assumptions of standard workflows. Data may evolve over time, exhibit hidden structure, or suffer from imbalance and noise. Models that perform well in controlled settings often degrade when exposed to dynamic environments. Deploying a model introduces additional challenges, including integration, monitoring, and continuous adaptation. This book addresses these realities.Purpose of This BookThe aim of this volume is to extend the modeling process beyond isolated techniques and toward complete, real-world systems. Rather than focusing on individual algorithms, the book emphasizes: modeling under temporal dependencediscovering structure in unlabeled datahandling imperfect and imbalanced datasetscombining methods into hybrid approachesdeploying models into operational environmentsapplying modeling techniques to real-world domainsWhat This Book CoversThis book is organized into eight parts, each addressing a key extension of the modeling framework.Part I - From Models to Systems introduces the broader perspective required for advanced data science. It examines the limitations of standard modeling assumptions and outlines how modeling fits into larger, dynamic systems.Part II - Time Series and Forecasting focuses on data with temporal structure. It covers foundational concepts, classical models such as ARIMA and SARIMA, and modern machine learning approaches to forecasting.Part III - Unsupervised Learning and Representation explores techniques for discovering structure without labeled data, including clustering, dimensionality reduction, and representation learning methods such as autoencoders.Part IV - Handling Real-World Data Challenges addresses practical issues that frequently arise in applied settings, with particular emphasis on imbalanced data and its impact on evaluation and model performance.Part V - Advanced and Hybrid Modeling examines strategies for combining models and integrating statistical and machine learning approaches to achieve improved performance and flexibility.Part VI - Deployment and Production Systems moves beyond model development to operational considerations, including model packaging, API construction, deployment pipelines, monitoring, and model maintenance.Part VII - Business Applications demonstrates how modeling techniques are applied in practice, with examples in business decision-making, financial forecasting, and customer segmentation.Part VIII - End-to-End Framework synthesizes the material into a unified perspective, providing a practical reference for designing, evaluating, and maintaining complete data science systems.Final RemarksAs models become more sophisticated, the challenges shift from implementation to design, evaluation, and integration.The central idea of this book is simple: "effective data science requires not only good models, but well-designed systems." By extending the modeling framework into more realistic and demanding settings, this book aims to provide the tools and perspective needed to move from isolated models to reliable, real-world solutions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…