Isbn: 9798188639730 - applied machine learning: solving real-world problems with regression and classification (4 resultados)

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Taschenbuch. Condición: Neu. Neuware - Data is everywhere, but turning it into useful predictions is where the real work begins.Applied Machine Learning takes you through the complete process of building practical predictive solutions-from understanding a business question and preparing messy information to training, evaluating, improving, and presenting a reliable model.Instead of focusing only on theory, this guide shows you how each decision affects the final result.Inside, you'll discover how to: - Define a clear prediction objective- Clean missing, inconsistent, and unusual values- Prepare numerical and categorical features- Choose suitable algorithms for continuous and categorical outcomes- Split datasets without creating information leakage- Compare models using meaningful evaluation metrics- Handle overfitting, class imbalance, and weak features- Tune performance without making the process unnecessarily complicated- Explain results to technical and nontechnical audiences- Turn experiments into repeatable workflowsYou'll explore practical examples such as estimating prices, predicting customer behaviour, identifying risk, and assigning observations to useful groups.The explanations are conversational, beginner-friendly, and focused on helping you understand why each step matters. You won't simply copy code and accept the output. You'll learn how to question the data, interpret results, recognize misleading performance, and decide whether a model is truly useful.Whether you're a student, analyst, programmer, researcher, or professional entering data science, this book will help you move from raw information to dependable predictions with greater confidence.…

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Paperback. Condición: new. Paperback. Data is everywhere, but turning it into useful predictions is where the real work begins.Applied Machine Learning takes you through the complete process of building practical predictive solutions-from understanding a business question and preparing messy information to training, evaluating, improving, and presenting a reliable model.Instead of focusing only on theory, this guide shows you how each decision affects the final result.Inside, you'll discover how to: - Define a clear prediction objective- Clean missing, inconsistent, and unusual values- Prepare numerical and categorical features- Choose suitable algorithms for continuous and categorical outcomes- Split datasets without creating information leakage- Compare models using meaningful evaluation metrics- Handle overfitting, class imbalance, and weak features- Tune performance without making the process unnecessarily complicated- Explain results to technical and nontechnical audiences- Turn experiments into repeatable workflowsYou'll explore practical examples such as estimating prices, predicting customer behaviour, identifying risk, and assigning observations to useful groups.The explanations are conversational, beginner-friendly, and focused on helping you understand why each step matters. You won't simply copy code and accept the output. You'll learn how to question the data, interpret results, recognize misleading performance, and decide whether a model is truly useful.Whether you're a student, analyst, programmer, researcher, or professional entering data science, this book will help you move from raw information to dependable predictions with greater 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.…