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Sprache: Englisch
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Sprache: Englisch
Verlag: Deepsim Press, 2026
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Sprache: Englisch
Verlag: Deepsim Press, 2026
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Paperback. Zustand: 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 i…ntroduces 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.
Sprache: Englisch
Verlag: Deepsim Press, 2026
- Softcover
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Paperback. Zustand: 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 i…s 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.
